1-3. 度数分布とヒストグラム
library(plotly)
#性別T1の度数分布とヒストグラム
gender_T1_count <- dplyr::count(data, gender_T1)
knitr::kable(gender_T1_count) #テーブル化
a <- ggplot(data = data, mapping = aes(x = gender_T1, fill = factor(gender_T1))) + geom_bar() #視覚化
ggplotly(a) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#性別T2の度数分布とヒストグラム
gender_T2_count <- dplyr::count(data, gender_T2)
knitr::kable(gender_T2_count) #テーブル化
b <- ggplot(data = data, mapping = aes(x = gender_T2, fill = factor(gender_T2))) + geom_bar() #視覚化
ggplotly(b) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#性別T3の度数分布とヒストグラム
gender_T3_count <- dplyr::count(data, gender_T3)
knitr::kable(gender_T3_count) #テーブル化
c <- ggplot(data = data, mapping = aes(x = gender_T3, fill = factor(gender_T3))) + geom_bar() #視覚化
ggplotly(c) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#性別T4の度数分布とヒストグラム
gender_T4_count <- dplyr::count(data, gender_T4)
knitr::kable(gender_T4_count) #テーブル化
d <- ggplot(data = data, mapping = aes(x = gender_T4, fill = factor(gender_T4))) + geom_bar() #視覚化
ggplotly(d) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc1_T1の度数分布とヒストグラム
hsc1_T1_count <- dplyr::count(data, hsc1_T1)
knitr::kable(hsc1_T1_count) #テーブル化
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e <- ggplot(data = data, mapping = aes(x = hsc1_T1, fill = factor(hsc1_T1))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(e) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc2_T1の度数分布とヒストグラム
hsc2_T1_count <- dplyr::count(data, hsc2_T1)
knitr::kable(hsc2_T1_count) #テーブル化
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f <- ggplot(data = data, mapping = aes(x = hsc2_T1, fill = factor(hsc2_T1))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(f) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc3_T1の度数分布とヒストグラム
hsc3_T1_count <- dplyr::count(data, hsc3_T1)
knitr::kable(hsc3_T1_count) #テーブル化
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g <- ggplot(data = data, mapping = aes(x = hsc3_T1, fill = factor(hsc3_T1))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(g) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc4_T1の度数分布とヒストグラム
hsc4_T1_count <- dplyr::count(data, hsc4_T1)
knitr::kable(hsc4_T1_count) #テーブル化
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h <- ggplot(data = data, mapping = aes(x = hsc4_T1, fill = factor(hsc4_T1))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(h) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc5_T1の度数分布とヒストグラム
hsc5_T1_count <- dplyr::count(data, hsc5_T1)
knitr::kable(hsc5_T1_count) #テーブル化
i <- ggplot(data = data, mapping = aes(x = hsc5_T1, fill = factor(hsc5_T1))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(i) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc6_T1の度数分布とヒストグラム
hsc6_T1_count <- dplyr::count(data, hsc6_T1)
knitr::kable(hsc6_T1_count) #テーブル化
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j <- ggplot(data = data, mapping = aes(x = hsc6_T1, fill = factor(hsc6_T1))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(j) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc7_T1の度数分布とヒストグラム
hsc7_T1_count <- dplyr::count(data, hsc7_T1)
knitr::kable(hsc7_T1_count) #テーブル化
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k <- ggplot(data = data, mapping = aes(x = hsc7_T1, fill = factor(hsc7_T1))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(k) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc8_T1の度数分布とヒストグラム
hsc8_T1_count <- dplyr::count(data, hsc8_T1)
knitr::kable(hsc8_T1_count) #テーブル化
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l <- ggplot(data = data, mapping = aes(x = hsc8_T1, fill = factor(hsc8_T1))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(l) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc9_T1の度数分布とヒストグラム
hsc9_T1_count <- dplyr::count(data, hsc9_T1)
knitr::kable(hsc9_T1_count) #テーブル化
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m <- ggplot(data = data, mapping = aes(x = hsc9_T1, fill = factor(hsc9_T1))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(m) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc10_T1の度数分布とヒストグラム
hsc10_T1_count <- dplyr::count(data, hsc10_T1)
knitr::kable(hsc10_T1_count) #テーブル化
n <- ggplot(data = data, mapping = aes(x = hsc10_T1, fill = factor(hsc10_T1))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(n) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc11_T1の度数分布とヒストグラム
hsc11_T1_count <- dplyr::count(data, hsc11_T1)
knitr::kable(hsc11_T1_count) #テーブル化
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o <- ggplot(data = data, mapping = aes(x = hsc11_T1, fill = factor(hsc11_T1))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(o) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc12_T1の度数分布とヒストグラム
hsc12_T1_count <- dplyr::count(data, hsc12_T1)
knitr::kable(hsc12_T1_count) #テーブル化
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p <- ggplot(data = data, mapping = aes(x = hsc12_T1, fill = factor(hsc12_T1))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(p) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc1_T2の度数分布とヒストグラム
hsc1_T2_count <- dplyr::count(data, hsc1_T2)
knitr::kable(hsc1_T2_count) #テーブル化
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q <- ggplot(data = data, mapping = aes(x = hsc1_T2, fill = factor(hsc1_T2))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(q) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc2_T2の度数分布とヒストグラム
hsc2_T2_count <- dplyr::count(data, hsc2_T2)
knitr::kable(hsc2_T2_count) #テーブル化
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r <- ggplot(data = data, mapping = aes(x = hsc2_T2, fill = factor(hsc2_T2))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(r) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc3_T2の度数分布とヒストグラム
hsc3_T2_count <- dplyr::count(data, hsc3_T2)
knitr::kable(hsc3_T2_count) #テーブル化
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| NA |
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s <- ggplot(data = data, mapping = aes(x = hsc3_T2, fill = factor(hsc3_T2))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(s) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc4_T2の度数分布とヒストグラム
hsc4_T2_count <- dplyr::count(data, hsc4_T2)
knitr::kable(hsc4_T2_count) #テーブル化
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| NA |
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t <- ggplot(data = data, mapping = aes(x = hsc4_T2, fill = factor(hsc4_T2))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(t) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc5_T2の度数分布とヒストグラム
hsc5_T2_count <- dplyr::count(data, hsc5_T2)
knitr::kable(hsc5_T2_count) #テーブル化
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| NA |
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u <- ggplot(data = data, mapping = aes(x = hsc5_T2, fill = factor(hsc5_T2))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(u) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc6_T2の度数分布とヒストグラム
hsc6_T2_count <- dplyr::count(data, hsc6_T2)
knitr::kable(hsc6_T2_count) #テーブル化
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v <- ggplot(data = data, mapping = aes(x = hsc6_T2, fill = factor(hsc6_T2))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(v) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc7_T2の度数分布とヒストグラム
hsc7_T2_count <- dplyr::count(data, hsc7_T2)
knitr::kable(hsc7_T2_count) #テーブル化
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w <- ggplot(data = data, mapping = aes(x = hsc7_T2, fill = factor(hsc7_T2))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(w) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc8_T2の度数分布とヒストグラム
hsc8_T2_count <- dplyr::count(data, hsc8_T2)
knitr::kable(hsc8_T2_count) #テーブル化
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neko <- ggplot(data = data, mapping = aes(x = hsc8_T2, fill = factor(hsc8_T2))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(neko) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc9_T2の度数分布とヒストグラム
hsc9_T2_count <- dplyr::count(data, hsc9_T2)
knitr::kable(hsc9_T2_count) #テーブル化
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| NA |
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y <- ggplot(data = data, mapping = aes(x = hsc9_T2, fill = factor(hsc9_T2))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(y) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc10_T2の度数分布とヒストグラム
hsc10_T2_count <- dplyr::count(data, hsc10_T2)
knitr::kable(hsc10_T2_count) #テーブル化
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| NA |
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z <- ggplot(data = data, mapping = aes(x = hsc10_T2, fill = factor(hsc10_T2))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(z) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc11_T2の度数分布とヒストグラム
hsc11_T2_count <- dplyr::count(data, hsc11_T2)
knitr::kable(hsc11_T2_count) #テーブル化
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aa <- ggplot(data = data, mapping = aes(x = hsc11_T2, fill = factor(hsc11_T2))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(aa) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc12_T2の度数分布とヒストグラム
hsc12_T2_count <- dplyr::count(data, hsc12_T2)
knitr::kable(hsc12_T2_count) #テーブル化
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bb <- ggplot(data = data, mapping = aes(x = hsc12_T2, fill = factor(hsc12_T2))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(bb) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc1_T3の度数分布とヒストグラム
hsc1_T3_count <- dplyr::count(data, hsc1_T3)
knitr::kable(hsc1_T3_count) #テーブル化
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cc <- ggplot(data = data, mapping = aes(x = hsc1_T3, fill = factor(hsc1_T3))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(cc) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc2_T3の度数分布とヒストグラム
hsc2_T3_count <- dplyr::count(data, hsc2_T3)
knitr::kable(hsc2_T3_count) #テーブル化
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| NA |
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dd <- ggplot(data = data, mapping = aes(x = hsc2_T3, fill = factor(hsc2_T3))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(dd) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc3_T3の度数分布とヒストグラム
hsc3_T3_count <- dplyr::count(data, hsc3_T3)
knitr::kable(hsc3_T3_count) #テーブル化
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| NA |
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ff <- ggplot(data = data, mapping = aes(x = hsc3_T3, fill = factor(hsc3_T3))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(ff) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc4_T3の度数分布とヒストグラム
hsc4_T3_count <- dplyr::count(data, hsc4_T3)
knitr::kable(hsc4_T3_count) #テーブル化
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| NA |
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gg <- ggplot(data = data, mapping = aes(x = hsc4_T3, fill = factor(hsc4_T3))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(gg) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc5_T3の度数分布とヒストグラム
hsc5_T3_count <- dplyr::count(data, hsc5_T3)
knitr::kable(hsc5_T3_count) #テーブル化
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| NA |
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hh <- ggplot(data = data, mapping = aes(x = hsc5_T3, fill = factor(hsc5_T3))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(hh) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc6_T3の度数分布とヒストグラム
hsc6_T3_count <- dplyr::count(data, hsc6_T3)
knitr::kable(hsc6_T3_count) #テーブル化
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| NA |
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ii <- ggplot(data = data, mapping = aes(x = hsc6_T3, fill = factor(hsc6_T3))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(ii) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc7_T3の度数分布とヒストグラム
hsc7_T3_count <- dplyr::count(data, hsc7_T3)
knitr::kable(hsc7_T3_count) #テーブル化
| 1 |
5 |
| 2 |
11 |
| 3 |
8 |
| 4 |
22 |
| 5 |
13 |
| 6 |
23 |
| 7 |
23 |
| NA |
9 |
jj <- ggplot(data = data, mapping = aes(x = hsc7_T3, fill = factor(hsc7_T3))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(jj) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc8_T3の度数分布とヒストグラム
hsc8_T3_count <- dplyr::count(data, hsc8_T3)
knitr::kable(hsc8_T3_count) #テーブル化
| 1 |
2 |
| 2 |
5 |
| 3 |
15 |
| 4 |
23 |
| 5 |
29 |
| 6 |
18 |
| 7 |
13 |
| NA |
9 |
kk <- ggplot(data = data, mapping = aes(x = hsc8_T3, fill = factor(hsc8_T3))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(kk) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc9_T3の度数分布とヒストグラム
hsc9_T3_count <- dplyr::count(data, hsc9_T3)
knitr::kable(hsc9_T3_count) #テーブル化
| 1 |
6 |
| 2 |
15 |
| 3 |
16 |
| 4 |
29 |
| 5 |
25 |
| 6 |
10 |
| 7 |
4 |
| NA |
9 |
ll <- ggplot(data = data, mapping = aes(x = hsc9_T3, fill = factor(hsc9_T3))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(ll) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc10_T3の度数分布とヒストグラム
hsc10_T3_count <- dplyr::count(data, hsc10_T3)
knitr::kable(hsc10_T3_count) #テーブル化
| 3 |
1 |
| 4 |
3 |
| 5 |
10 |
| 6 |
29 |
| 7 |
62 |
| NA |
9 |
dog <- ggplot(data = data, mapping = aes(x = hsc10_T3, fill = factor(hsc10_T3))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(dog) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc11_T3の度数分布とヒストグラム
hsc11_T3_count <- dplyr::count(data, hsc11_T3)
knitr::kable(hsc11_T3_count) #テーブル化
| 1 |
3 |
| 2 |
4 |
| 3 |
10 |
| 4 |
22 |
| 5 |
25 |
| 6 |
25 |
| 7 |
16 |
| NA |
9 |
mm <- ggplot(data = data, mapping = aes(x = hsc11_T3, fill = factor(hsc11_T3))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(mm) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc12_T3の度数分布とヒストグラム
hsc12_T3_count <- dplyr::count(data, hsc12_T3)
knitr::kable(hsc12_T3_count) #テーブル化
| 2 |
1 |
| 3 |
2 |
| 4 |
9 |
| 5 |
27 |
| 6 |
39 |
| 7 |
27 |
| NA |
9 |
nn <- ggplot(data = data, mapping = aes(x = hsc12_T3, fill = factor(hsc12_T3))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(nn) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc1_T4の度数分布とヒストグラム
hsc1_T4_count <- dplyr::count(data, hsc1_T4)
knitr::kable(hsc1_T4_count) #テーブル化
| 1 |
1 |
| 2 |
2 |
| 3 |
15 |
| 4 |
17 |
| 5 |
41 |
| 6 |
27 |
| 7 |
3 |
| NA |
8 |
oo <- ggplot(data = data, mapping = aes(x = hsc1_T4, fill = factor(hsc1_T4))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(oo) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc2_T4の度数分布とヒストグラム
hsc2_T4_count <- dplyr::count(data, hsc2_T4)
knitr::kable(hsc2_T4_count) #テーブル化
| 2 |
9 |
| 3 |
11 |
| 4 |
9 |
| 5 |
32 |
| 6 |
32 |
| 7 |
13 |
| NA |
8 |
pp <- ggplot(data = data, mapping = aes(x = hsc2_T4, fill = factor(hsc2_T4))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(pp) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc3_T4の度数分布とヒストグラム
hsc3_T4_count <- dplyr::count(data, hsc3_T4)
knitr::kable(hsc3_T4_count) #テーブル化
| 2 |
2 |
| 3 |
2 |
| 4 |
9 |
| 5 |
21 |
| 6 |
29 |
| 7 |
43 |
| NA |
8 |
qq <- ggplot(data = data, mapping = aes(x = hsc3_T4, fill = factor(hsc3_T4))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(qq) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc4_T4の度数分布とヒストグラム
hsc4_T4_count <- dplyr::count(data, hsc4_T4)
knitr::kable(hsc4_T4_count) #テーブル化
| 1 |
5 |
| 2 |
5 |
| 3 |
13 |
| 4 |
12 |
| 5 |
35 |
| 6 |
22 |
| 7 |
14 |
| NA |
8 |
rr <- ggplot(data = data, mapping = aes(x = hsc4_T4, fill = factor(hsc4_T4))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(rr) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc5_T4の度数分布とヒストグラム
hsc5_T4_count <- dplyr::count(data, hsc5_T4)
knitr::kable(hsc5_T4_count) #テーブル化
| 2 |
1 |
| 3 |
4 |
| 4 |
3 |
| 5 |
19 |
| 6 |
23 |
| 7 |
54 |
| NA |
10 |
ss <- ggplot(data = data, mapping = aes(x = hsc5_T4, fill = factor(hsc5_T4))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(ss) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc6_T4の度数分布とヒストグラム
hsc6_T4_count <- dplyr::count(data, hsc6_T4)
knitr::kable(hsc6_T4_count) #テーブル化
| 1 |
2 |
| 2 |
8 |
| 3 |
8 |
| 4 |
9 |
| 5 |
38 |
| 6 |
29 |
| 7 |
12 |
| NA |
8 |
inu <- ggplot(data = data, mapping = aes(x = hsc6_T4, fill = factor(hsc6_T4))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(inu) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc7_T4の度数分布とヒストグラム
hsc7_T4_count <- dplyr::count(data, hsc7_T4)
knitr::kable(hsc7_T4_count) #テーブル化
| 1 |
4 |
| 2 |
9 |
| 3 |
11 |
| 4 |
16 |
| 5 |
18 |
| 6 |
27 |
| 7 |
21 |
| NA |
8 |
tt <- ggplot(data = data, mapping = aes(x = hsc7_T4, fill = factor(hsc7_T4))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(tt) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc8_T4の度数分布とヒストグラム
hsc8_T4_count <- dplyr::count(data, hsc8_T4)
knitr::kable(hsc8_T4_count) #テーブル化
| 1 |
2 |
| 2 |
5 |
| 3 |
17 |
| 4 |
20 |
| 5 |
34 |
| 6 |
17 |
| 7 |
11 |
| NA |
8 |
vv <- ggplot(data = data, mapping = aes(x = hsc8_T4, fill = factor(hsc8_T4))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(vv) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc9_T4の度数分布とヒストグラム
hsc9_T4_count <- dplyr::count(data, hsc9_T4)
knitr::kable(hsc9_T4_count) #テーブル化
| 1 |
6 |
| 2 |
13 |
| 3 |
19 |
| 4 |
30 |
| 5 |
24 |
| 6 |
11 |
| 7 |
3 |
| NA |
8 |
ww <- ggplot(data = data, mapping = aes(x = hsc9_T4, fill = factor(hsc9_T4))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(ww) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc10_T4の度数分布とヒストグラム
hsc10_T4_count <- dplyr::count(data, hsc10_T4)
knitr::kable(hsc10_T4_count) #テーブル化
| 2 |
1 |
| 3 |
1 |
| 4 |
3 |
| 5 |
11 |
| 6 |
30 |
| 7 |
60 |
| NA |
8 |
kame <- ggplot(data = data, mapping = aes(x = hsc10_T4, fill = factor(hsc10_T4))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(kame) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc11_T4の度数分布とヒストグラム
hsc11_T4_count <- dplyr::count(data, hsc11_T4)
knitr::kable(hsc11_T4_count) #テーブル化
| 1 |
2 |
| 2 |
10 |
| 3 |
11 |
| 4 |
18 |
| 5 |
28 |
| 6 |
22 |
| 7 |
15 |
| NA |
8 |
yy <- ggplot(data = data, mapping = aes(x = hsc11_T4, fill = factor(hsc11_T4))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(yy) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc12_T4の度数分布とヒストグラム
hsc12_T4_count <- dplyr::count(data, hsc12_T4)
knitr::kable(hsc12_T4_count) #テーブル化
| 1 |
1 |
| 2 |
5 |
| 3 |
2 |
| 4 |
7 |
| 5 |
31 |
| 6 |
31 |
| 7 |
29 |
| NA |
8 |
zz <- ggplot(data = data, mapping = aes(x = hsc12_T4, fill = factor(hsc12_T4))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(zz) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#wb1_T1の度数分布とヒストグラム
wb1_T1_count <- dplyr::count(data, wb1_T1)
knitr::kable(wb1_T1_count) #テーブル化
| 0 |
1 |
| 1 |
6 |
| 2 |
27 |
| 3 |
39 |
| 4 |
31 |
| 5 |
10 |
aaa <- ggplot(data = data, mapping = aes(x = wb1_T1, fill = factor(wb1_T1))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(aaa) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#wb2_T1の度数分布とヒストグラム
wb2_T1_count <- dplyr::count(data, wb2_T1)
knitr::kable(wb2_T1_count) #テーブル化
| 0 |
2 |
| 1 |
12 |
| 2 |
31 |
| 3 |
40 |
| 4 |
23 |
| 5 |
6 |
bbb <- ggplot(data = data, mapping = aes(x = wb2_T1, fill = factor(wb2_T1))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(bbb) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#wb3_T1の度数分布とヒストグラム
wb3_T1_count <- dplyr::count(data, wb3_T1)
knitr::kable(wb3_T1_count) #テーブル化
| 0 |
2 |
| 1 |
11 |
| 2 |
26 |
| 3 |
36 |
| 4 |
28 |
| 5 |
10 |
| NA |
1 |
ccc <- ggplot(data = data, mapping = aes(x = wb3_T1, fill = factor(wb3_T1))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(ccc) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#wb4_T1の度数分布とヒストグラム
wb4_T1_count <- dplyr::count(data, wb4_T1)
knitr::kable(wb4_T1_count) #テーブル化
| 0 |
4 |
| 1 |
35 |
| 2 |
34 |
| 3 |
27 |
| 4 |
9 |
| 5 |
5 |
ddd <- ggplot(data = data, mapping = aes(x = wb4_T1, fill = factor(wb4_T1))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(ddd) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#wb5_T1の度数分布とヒストグラム
wb5_T1_count <- dplyr::count(data, wb5_T1)
knitr::kable(wb5_T1_count) #テーブル化
| 0 |
3 |
| 1 |
15 |
| 2 |
29 |
| 3 |
38 |
| 4 |
22 |
| 5 |
7 |
eee <- ggplot(data = data, mapping = aes(x = wb5_T1, fill = factor(wb5_T1))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(eee) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#wb1_T2の度数分布とヒストグラム
wb1_T2_count <- dplyr::count(data, wb1_T2)
knitr::kable(wb1_T2_count) #テーブル化
| 1 |
5 |
| 2 |
23 |
| 3 |
30 |
| 4 |
33 |
| 5 |
9 |
| NA |
14 |
fff <- ggplot(data = data, mapping = aes(x = wb1_T2, fill = factor(wb1_T2))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(fff) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#wb2_T2の度数分布とヒストグラム
wb2_T2_count <- dplyr::count(data, wb2_T2)
knitr::kable(wb2_T2_count) #テーブル化
| 1 |
7 |
| 2 |
23 |
| 3 |
28 |
| 4 |
32 |
| 5 |
9 |
| NA |
15 |
ggg <- ggplot(data = data, mapping = aes(x = wb2_T2, fill = factor(wb2_T2))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(ggg) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#wb3_T2の度数分布とヒストグラム
wb3_T2_count <- dplyr::count(data, wb3_T2)
knitr::kable(wb3_T2_count) #テーブル化
| 1 |
10 |
| 2 |
16 |
| 3 |
36 |
| 4 |
25 |
| 5 |
13 |
| NA |
14 |
hhh <- ggplot(data = data, mapping = aes(x = wb3_T2, fill = factor(wb3_T2))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(hhh) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#wb4_T2の度数分布とヒストグラム
wb4_T2_count <- dplyr::count(data, wb4_T2)
knitr::kable(wb4_T2_count) #テーブル化
| 0 |
2 |
| 1 |
26 |
| 2 |
29 |
| 3 |
23 |
| 4 |
12 |
| 5 |
8 |
| NA |
14 |
iii <- ggplot(data = data, mapping = aes(x = wb4_T2, fill = factor(wb4_T2))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(iii) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#wb5_T2の度数分布とヒストグラム
wb5_T2_count <- dplyr::count(data, wb5_T2)
knitr::kable(wb5_T2_count) #テーブル化
| 1 |
12 |
| 2 |
26 |
| 3 |
28 |
| 4 |
21 |
| 5 |
13 |
| NA |
14 |
jjj <- ggplot(data = data, mapping = aes(x = wb5_T2, fill = factor(wb5_T2))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(jjj) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#wb1_T3の度数分布とヒストグラム
wb1_T3_count <- dplyr::count(data, wb1_T3)
knitr::kable(wb1_T3_count) #テーブル化
| 0 |
1 |
| 1 |
6 |
| 2 |
18 |
| 3 |
36 |
| 4 |
28 |
| 5 |
16 |
| NA |
9 |
kkk <- ggplot(data = data, mapping = aes(x = wb1_T3, fill = factor(wb1_T3))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(kkk) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#wb2_T3の度数分布とヒストグラム
wb2_T3_count <- dplyr::count(data, wb2_T3)
knitr::kable(wb2_T3_count) #テーブル化
| 0 |
1 |
| 1 |
12 |
| 2 |
22 |
| 3 |
32 |
| 4 |
27 |
| 5 |
11 |
| NA |
9 |
lll <- ggplot(data = data, mapping = aes(x = wb2_T3, fill = factor(wb2_T3))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(lll) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#wb3_T3の度数分布とヒストグラム
wb3_T3_count <- dplyr::count(data, wb3_T3)
knitr::kable(wb1_T3_count) #テーブル化
| 0 |
1 |
| 1 |
6 |
| 2 |
18 |
| 3 |
36 |
| 4 |
28 |
| 5 |
16 |
| NA |
9 |
mmm <- ggplot(data = data, mapping = aes(x = wb3_T3, fill = factor(wb3_T3))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(mmm) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#wb4_T3の度数分布とヒストグラム
wb4_T3_count <- dplyr::count(data, wb4_T3)
knitr::kable(wb4_T3_count) #テーブル化
| 0 |
6 |
| 1 |
20 |
| 2 |
40 |
| 3 |
18 |
| 4 |
15 |
| 5 |
6 |
| NA |
9 |
nnn <- ggplot(data = data, mapping = aes(x = wb4_T3, fill = factor(wb4_T3))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(nnn) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#wb5_T3の度数分布とヒストグラム
wb5_T3_count <- dplyr::count(data, wb5_T3)
knitr::kable(wb5_T3_count) #テーブル化
| 0 |
1 |
| 1 |
16 |
| 2 |
27 |
| 3 |
38 |
| 4 |
16 |
| 5 |
7 |
| NA |
9 |
ooo <- ggplot(data = data, mapping = aes(x = wb5_T3, fill = factor(wb5_T3))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(ooo) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#wb1_T4の度数分布とヒストグラム
wb1_T4_count <- dplyr::count(data, wb1_T4)
knitr::kable(wb1_T4_count) #テーブル化
| 0 |
1 |
| 1 |
10 |
| 2 |
17 |
| 3 |
34 |
| 4 |
34 |
| 5 |
10 |
| NA |
8 |
ppp <- ggplot(data = data, mapping = aes(x = wb1_T4, fill = factor(wb1_T4))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(ppp) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#wb2_T4の度数分布とヒストグラム
wb2_T4_count <- dplyr::count(data, wb2_T4)
knitr::kable(wb2_T4_count) #テーブル化
| 0 |
3 |
| 1 |
14 |
| 2 |
24 |
| 3 |
32 |
| 4 |
21 |
| 5 |
12 |
| NA |
8 |
qqq <- ggplot(data = data, mapping = aes(x = wb2_T4, fill = factor(wb2_T4))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(qqq) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#wb3_T4の度数分布とヒストグラム
wb3_T4_count <- dplyr::count(data, wb3_T4)
knitr::kable(wb3_T4_count) #テーブル化
| 0 |
1 |
| 1 |
9 |
| 2 |
25 |
| 3 |
31 |
| 4 |
23 |
| 5 |
17 |
| NA |
8 |
rrr <- ggplot(data = data, mapping = aes(x = wb3_T4, fill = factor(wb3_T4))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(rrr) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#wb5_T4の度数分布とヒストグラム
wb5_T4_count <- dplyr::count(data, wb5_T4)
knitr::kable(wb5_T4_count) #テーブル化
| 0 |
4 |
| 1 |
12 |
| 2 |
25 |
| 3 |
30 |
| 4 |
20 |
| 5 |
15 |
| NA |
8 |
sss <- ggplot(data = data, mapping = aes(x = wb5_T4, fill = factor(wb5_T4))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(sss) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#event1_T1の度数分布とヒストグラム
event1_T1_count <- dplyr::count(data, ev1_T1)
knitr::kable(event1_T1_count) #テーブル化
| -3 |
16 |
| -2 |
11 |
| -1 |
9 |
| 0 |
3 |
| 1 |
8 |
| 2 |
11 |
| 3 |
55 |
| NA |
1 |
ttt <- ggplot(data = data, mapping = aes(x = ev1_T1, fill = factor(ev1_T1))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(ttt) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#event2_T1の度数分布とヒストグラム
event2_T1_count <- dplyr::count(data, ev2_T1)
knitr::kable(event2_T1_count) #テーブル化
| -3 |
21 |
| -2 |
16 |
| -1 |
8 |
| 0 |
5 |
| 1 |
2 |
| 2 |
19 |
| 3 |
42 |
| NA |
1 |
uuu <- ggplot(data = data, mapping = aes(x = ev2_T1, fill = factor(ev2_T1))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(uuu) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#event1_T2の度数分布とヒストグラム
event1_T2_count <- dplyr::count(data, ev1_T2)
knitr::kable(event1_T2_count) #テーブル化
| -3 |
17 |
| -2 |
7 |
| -1 |
4 |
| 0 |
4 |
| 1 |
6 |
| 2 |
13 |
| 3 |
47 |
| NA |
16 |
vvv <- ggplot(data = data, mapping = aes(x = ev1_T2, fill = factor(ev1_T2))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(vvv) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#event2_T2の度数分布とヒストグラム
event2_T2_count <- dplyr::count(data, ev2_T2)
knitr::kable(event2_T2_count) #テーブル化
| -3 |
20 |
| -2 |
15 |
| -1 |
5 |
| 0 |
1 |
| 1 |
8 |
| 2 |
17 |
| 3 |
29 |
| NA |
19 |
www <- ggplot(data = data, mapping = aes(x = ev2_T2, fill = factor(ev2_T2))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(www) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#event1_T3の度数分布とヒストグラム
event1_T3_count <- dplyr::count(data, ev1_T3)
knitr::kable(event1_T3_count) #テーブル化
| -3 |
19 |
| -2 |
6 |
| -1 |
10 |
| 0 |
6 |
| 1 |
4 |
| 2 |
11 |
| 3 |
47 |
| NA |
11 |
usagi <- ggplot(data = data, mapping = aes(x = ev1_T3, fill = factor(ev1_T3))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(usagi) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#event2_T3の度数分布とヒストグラム
event2_T3_count <- dplyr::count(data, ev2_T3)
knitr::kable(event2_T3_count) #テーブル化
| -3 |
22 |
| -2 |
12 |
| -1 |
4 |
| 0 |
5 |
| 1 |
13 |
| 2 |
13 |
| 3 |
34 |
| NA |
11 |
yyy <- ggplot(data = data, mapping = aes(x = ev2_T3, fill = factor(ev2_T3))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(yyy) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#event1_T4の度数分布とヒストグラム
event1_T4_count <- dplyr::count(data, ev1_T4)
knitr::kable(event1_T4_count) #テーブル化
| -3 |
15 |
| -2 |
5 |
| -1 |
3 |
| 0 |
4 |
| 1 |
10 |
| 2 |
15 |
| 3 |
49 |
| NA |
13 |
zzz <- ggplot(data = data, mapping = aes(x = ev1_T4, fill = factor(ev1_T4))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(zzz) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#event2_T4の度数分布とヒストグラム
event2_T4_count <- dplyr::count(data, ev2_T4)
knitr::kable(event2_T4_count) #テーブル化
| -3 |
15 |
| -2 |
13 |
| -1 |
10 |
| 0 |
9 |
| 1 |
9 |
| 2 |
14 |
| 3 |
31 |
| NA |
13 |
aaaa <- ggplot(data = data, mapping = aes(x = ev2_T4, fill = factor(ev2_T4))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(aaaa) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#eoe_T1の度数分布とヒストグラム
eoe_T1_count <- dplyr::count(data, eoe_T1)
knitr::kable(eoe_T1_count) #テーブル化
| 1.8 |
2 |
| 2.2 |
1 |
| 2.4 |
2 |
| 2.6 |
1 |
| 2.8 |
1 |
| 3.0 |
1 |
| 3.2 |
3 |
| 3.4 |
5 |
| 3.6 |
2 |
| 3.8 |
10 |
| 4.0 |
6 |
| 4.2 |
14 |
| 4.4 |
6 |
| 4.6 |
7 |
| 4.8 |
11 |
| 5.0 |
9 |
| 5.2 |
10 |
| 5.4 |
5 |
| 5.6 |
6 |
| 5.8 |
5 |
| 6.0 |
4 |
| 6.4 |
3 |
bbbb <- ggplot(data = data, mapping = aes(x = eoe_T1, fill = factor(eoe_T1))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(bbbb) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#eoe_T2の度数分布とヒストグラム
eoe_T2_count <- dplyr::count(data, eoe_T2)
knitr::kable(eoe_T2_count) #テーブル化
| 1.8 |
2 |
| 2.0 |
1 |
| 2.4 |
1 |
| 2.6 |
2 |
| 2.8 |
2 |
| 3.0 |
1 |
| 3.2 |
2 |
| 3.4 |
2 |
| 3.6 |
2 |
| 3.8 |
4 |
| 4.0 |
5 |
| 4.2 |
8 |
| 4.4 |
6 |
| 4.6 |
9 |
| 4.8 |
16 |
| 5.0 |
3 |
| 5.2 |
7 |
| 5.4 |
5 |
| 5.6 |
9 |
| 5.8 |
2 |
| 6.0 |
3 |
| 6.2 |
4 |
| 6.4 |
1 |
| 6.6 |
1 |
| 6.8 |
1 |
| NA |
15 |
cccc <- ggplot(data = data, mapping = aes(x = eoe_T2, fill = factor(eoe_T2))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(cccc) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#eoe_T3の度数分布とヒストグラム
eoe_T3_count <- dplyr::count(data, eoe_T3)
knitr::kable(eoe_T3_count) #テーブル化
| 2.0 |
1 |
| 2.2 |
1 |
| 2.4 |
1 |
| 2.6 |
1 |
| 3.0 |
2 |
| 3.2 |
1 |
| 3.6 |
1 |
| 3.8 |
4 |
| 4.0 |
3 |
| 4.2 |
5 |
| 4.4 |
7 |
| 4.6 |
10 |
| 4.8 |
8 |
| 5.0 |
14 |
| 5.2 |
11 |
| 5.4 |
11 |
| 5.6 |
7 |
| 5.8 |
3 |
| 6.0 |
2 |
| 6.2 |
2 |
| 6.4 |
5 |
| 6.6 |
2 |
| 7.0 |
2 |
| NA |
10 |
dddd <- ggplot(data = data, mapping = aes(x = eoe_T3, fill = factor(eoe_T3))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(dddd) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#eoe_T4の度数分布とヒストグラム
eoe_T4_count <- dplyr::count(data, eoe_T4)
knitr::kable(eoe_T4_count) #テーブル化
| 1.0 |
1 |
| 2.0 |
1 |
| 2.2 |
1 |
| 2.4 |
1 |
| 2.6 |
1 |
| 2.8 |
3 |
| 3.0 |
2 |
| 3.2 |
3 |
| 3.4 |
1 |
| 3.6 |
1 |
| 3.8 |
3 |
| 4.0 |
6 |
| 4.2 |
2 |
| 4.4 |
7 |
| 4.6 |
13 |
| 4.8 |
7 |
| 5.0 |
7 |
| 5.2 |
10 |
| 5.4 |
7 |
| 5.6 |
11 |
| 5.8 |
6 |
| 6.0 |
5 |
| 6.2 |
2 |
| 6.4 |
2 |
| 6.6 |
1 |
| 6.8 |
1 |
| 7.0 |
1 |
| NA |
8 |
eeee <- ggplot(data = data, mapping = aes(x = eoe_T4, fill = factor(eoe_T4))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(eeee) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#lst_T1の度数分布とヒストグラム
lst_T1_count <- dplyr::count(data, lst_T1)
knitr::kable(lst_T1_count) #テーブル化
| 1.5 |
2 |
| 2.0 |
3 |
| 2.5 |
3 |
| 3.0 |
7 |
| 3.5 |
15 |
| 4.0 |
8 |
| 4.5 |
14 |
| 5.0 |
15 |
| 5.5 |
16 |
| 6.0 |
10 |
| 6.5 |
9 |
| 7.0 |
12 |
ffff <- ggplot(data = data, mapping = aes(x = lst_T1, fill = factor(lst_T1))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(ffff) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#lst_T2の度数分布とヒストグラム
lst_T2_count <- dplyr::count(data, lst_T2)
knitr::kable(lst_T2_count) #テーブル化
| 1.0 |
1 |
| 2.0 |
6 |
| 2.5 |
4 |
| 3.0 |
5 |
| 3.5 |
6 |
| 4.0 |
9 |
| 4.5 |
11 |
| 5.0 |
18 |
| 5.5 |
13 |
| 6.0 |
12 |
| 6.5 |
7 |
| 7.0 |
7 |
| NA |
15 |
gggg <- ggplot(data = data, mapping = aes(x = lst_T2, fill = factor(lst_T2))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(gggg) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#lst_T3の度数分布とヒストグラム
lst_T3_count <- dplyr::count(data, lst_T3)
knitr::kable(lst_T3_count) #テーブル化
| 1.0 |
1 |
| 1.5 |
1 |
| 2.0 |
2 |
| 2.5 |
1 |
| 3.0 |
8 |
| 3.5 |
5 |
| 4.0 |
13 |
| 4.5 |
7 |
| 5.0 |
17 |
| 5.5 |
13 |
| 6.0 |
19 |
| 6.5 |
7 |
| 7.0 |
11 |
| NA |
9 |
hhhh <- ggplot(data = data, mapping = aes(x = lst_T3, fill = factor(lst_T3))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(hhhh) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#lst_T4の度数分布とヒストグラム
lst_T4_count <- dplyr::count(data, lst_T4)
knitr::kable(lst_T4_count) #テーブル化
| 2.0 |
6 |
| 2.5 |
3 |
| 3.0 |
9 |
| 3.5 |
4 |
| 4.0 |
10 |
| 4.5 |
8 |
| 5.0 |
22 |
| 5.5 |
11 |
| 6.0 |
18 |
| 6.5 |
6 |
| 7.0 |
9 |
| NA |
8 |
iiii <- ggplot(data = data, mapping = aes(x = lst_T4, fill = factor(lst_T4))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(iiii) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#aes_T1の度数分布とヒストグラム
aes_T1_count <- dplyr::count(data, aes_T1)
knitr::kable(aes_T1_count) #テーブル化
| 3.25 |
1 |
| 4.25 |
1 |
| 4.50 |
2 |
| 4.75 |
6 |
| 5.00 |
6 |
| 5.25 |
15 |
| 5.50 |
9 |
| 5.75 |
22 |
| 6.00 |
21 |
| 6.25 |
11 |
| 6.50 |
9 |
| 6.75 |
5 |
| 7.00 |
5 |
| NA |
1 |
jjjj <- ggplot(data = data, mapping = aes(x = aes_T1, fill = factor(aes_T1))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(jjjj) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#aes_T2の度数分布とヒストグラム
aes_T2_count <- dplyr::count(data, aes_T2)
knitr::kable(aes_T2_count) #テーブル化
| 2.50 |
1 |
| 3.50 |
1 |
| 4.00 |
1 |
| 4.50 |
3 |
| 4.75 |
3 |
| 5.00 |
10 |
| 5.25 |
12 |
| 5.50 |
15 |
| 5.75 |
11 |
| 6.00 |
13 |
| 6.25 |
15 |
| 6.50 |
6 |
| 6.75 |
6 |
| 7.00 |
3 |
| NA |
14 |
kkkk <- ggplot(data = data, mapping = aes(x = aes_T2, fill = factor(aes_T2))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(kkkk) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#aes_T3の度数分布とヒストグラム
aes_T3_count <- dplyr::count(data, aes_T3)
knitr::kable(aes_T3_count) #テーブル化
| 4.00 |
2 |
| 4.25 |
1 |
| 4.50 |
2 |
| 4.75 |
1 |
| 5.00 |
10 |
| 5.25 |
9 |
| 5.50 |
13 |
| 5.75 |
15 |
| 6.00 |
9 |
| 6.25 |
17 |
| 6.50 |
14 |
| 6.75 |
7 |
| 7.00 |
5 |
| NA |
9 |
llll <- ggplot(data = data, mapping = aes(x = aes_T3, fill = factor(aes_T3))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(llll) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#aes_T4の度数分布とヒストグラム
aes_T4_count <- dplyr::count(data, aes_T4)
knitr::kable(aes_T4_count) #テーブル化
| 2.75 |
2 |
| 3.50 |
1 |
| 4.25 |
2 |
| 4.50 |
2 |
| 4.75 |
4 |
| 5.00 |
5 |
| 5.25 |
10 |
| 5.50 |
12 |
| 5.75 |
10 |
| 6.00 |
16 |
| 6.25 |
13 |
| 6.50 |
12 |
| 6.75 |
14 |
| 7.00 |
1 |
| NA |
10 |
mmmm <- ggplot(data = data, mapping = aes(x = aes_T4, fill = factor(aes_T4))) + geom_histogram(binwidth = 1) #視覚化
ggplotly(mmmm) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc_T1の度数分布とヒストグラム
hsc_T1_count <- dplyr::count(data, hsc_T1)
knitr::kable(hsc_T1_count) #テーブル化
| 3.183333 |
1 |
| 3.300000 |
1 |
| 3.383333 |
1 |
| 3.400000 |
1 |
| 3.416667 |
1 |
| 3.650000 |
1 |
| 3.766667 |
1 |
| 3.933333 |
1 |
| 3.966667 |
1 |
| 3.983333 |
1 |
| 4.016667 |
1 |
| 4.066667 |
1 |
| 4.116667 |
1 |
| 4.150000 |
1 |
| 4.183333 |
1 |
| 4.266667 |
1 |
| 4.283333 |
1 |
| 4.316667 |
1 |
| 4.350000 |
2 |
| 4.383333 |
1 |
| 4.433333 |
1 |
| 4.450000 |
1 |
| 4.483333 |
3 |
| 4.500000 |
2 |
| 4.516667 |
1 |
| 4.633333 |
1 |
| 4.650000 |
1 |
| 4.683333 |
1 |
| 4.733333 |
1 |
| 4.750000 |
2 |
| 4.766667 |
2 |
| 4.800000 |
1 |
| 4.816667 |
1 |
| 4.850000 |
5 |
| 4.866667 |
1 |
| 4.883333 |
1 |
| 4.916667 |
1 |
| 4.933333 |
1 |
| 5.000000 |
2 |
| 5.016667 |
1 |
| 5.050000 |
1 |
| 5.066667 |
1 |
| 5.116667 |
1 |
| 5.150000 |
1 |
| 5.166667 |
2 |
| 5.200000 |
1 |
| 5.216667 |
2 |
| 5.233333 |
1 |
| 5.250000 |
1 |
| 5.283333 |
2 |
| 5.300000 |
1 |
| 5.316667 |
3 |
| 5.333333 |
2 |
| 5.366667 |
1 |
| 5.383333 |
1 |
| 5.416667 |
1 |
| 5.433333 |
2 |
| 5.450000 |
1 |
| 5.466667 |
1 |
| 5.483333 |
3 |
| 5.500000 |
1 |
| 5.516667 |
2 |
| 5.533333 |
1 |
| 5.566667 |
3 |
| 5.583333 |
1 |
| 5.616667 |
3 |
| 5.666667 |
1 |
| 5.683333 |
1 |
| 5.700000 |
1 |
| 5.733333 |
2 |
| 5.766667 |
1 |
| 5.800000 |
4 |
| 5.833333 |
1 |
| 5.850000 |
1 |
| 5.900000 |
1 |
| 5.916667 |
1 |
| 6.066667 |
1 |
| 6.083333 |
1 |
| 6.116667 |
1 |
| 6.133333 |
1 |
| 6.300000 |
1 |
| 6.383333 |
1 |
| 6.466667 |
1 |
| 6.600000 |
1 |
| NA |
1 |
nnnn <- ggplot(data = data, mapping = aes(x = hsc_T1, fill = factor(hsc_T1))) + geom_histogram(binwidth = 0.1) #視覚化
ggplotly(nnnn) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc_T2の度数分布とヒストグラム
hsc_T2_count <- dplyr::count(data, hsc_T2)
knitr::kable(hsc_T2_count) #テーブル化
| 2.533333 |
1 |
| 2.916667 |
1 |
| 3.066667 |
1 |
| 3.600000 |
1 |
| 3.616667 |
1 |
| 3.716667 |
1 |
| 3.850000 |
1 |
| 4.000000 |
1 |
| 4.016667 |
1 |
| 4.033333 |
1 |
| 4.050000 |
1 |
| 4.100000 |
2 |
| 4.166667 |
2 |
| 4.233333 |
1 |
| 4.316667 |
1 |
| 4.400000 |
1 |
| 4.433333 |
1 |
| 4.466667 |
1 |
| 4.500000 |
1 |
| 4.516667 |
3 |
| 4.533333 |
1 |
| 4.550000 |
2 |
| 4.566667 |
1 |
| 4.633333 |
1 |
| 4.650000 |
1 |
| 4.733333 |
2 |
| 4.766667 |
1 |
| 4.783333 |
2 |
| 4.866667 |
1 |
| 4.883333 |
2 |
| 4.900000 |
1 |
| 4.933333 |
2 |
| 4.950000 |
1 |
| 4.983333 |
1 |
| 5.000000 |
1 |
| 5.016667 |
1 |
| 5.066667 |
1 |
| 5.100000 |
1 |
| 5.116667 |
1 |
| 5.150000 |
2 |
| 5.183333 |
2 |
| 5.200000 |
2 |
| 5.233333 |
2 |
| 5.250000 |
1 |
| 5.283333 |
1 |
| 5.300000 |
1 |
| 5.333333 |
1 |
| 5.350000 |
1 |
| 5.383333 |
1 |
| 5.416667 |
2 |
| 5.433333 |
2 |
| 5.450000 |
1 |
| 5.466667 |
1 |
| 5.516667 |
1 |
| 5.533333 |
1 |
| 5.616667 |
2 |
| 5.633333 |
1 |
| 5.666667 |
2 |
| 5.683333 |
3 |
| 5.700000 |
3 |
| 5.766667 |
1 |
| 5.783333 |
1 |
| 5.800000 |
1 |
| 5.816667 |
1 |
| 5.866667 |
1 |
| 5.883333 |
1 |
| 5.950000 |
1 |
| 6.016667 |
2 |
| 6.066667 |
2 |
| 6.150000 |
1 |
| 6.233333 |
2 |
| 6.283333 |
1 |
| 6.683333 |
1 |
| 6.800000 |
1 |
| NA |
16 |
oooo <- ggplot(data = data, mapping = aes(x = hsc_T2, fill = factor(hsc_T2))) + geom_histogram(binwidth = 0.1) #視覚化
ggplotly(oooo) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc_T3の度数分布とヒストグラム
hsc_T3_count <- dplyr::count(data, hsc_T3)
knitr::kable(hsc_T3_count) #テーブル化
| 3.383333 |
1 |
| 3.483333 |
1 |
| 3.500000 |
1 |
| 3.766667 |
1 |
| 3.916667 |
1 |
| 3.950000 |
1 |
| 4.000000 |
1 |
| 4.016667 |
1 |
| 4.083333 |
1 |
| 4.150000 |
1 |
| 4.216667 |
1 |
| 4.283333 |
1 |
| 4.400000 |
1 |
| 4.433333 |
1 |
| 4.450000 |
1 |
| 4.483333 |
1 |
| 4.533333 |
1 |
| 4.566667 |
2 |
| 4.700000 |
2 |
| 4.766667 |
2 |
| 4.800000 |
1 |
| 4.866667 |
2 |
| 4.883333 |
1 |
| 4.916667 |
2 |
| 4.933333 |
1 |
| 4.950000 |
1 |
| 4.966667 |
2 |
| 4.983333 |
2 |
| 5.000000 |
1 |
| 5.033333 |
2 |
| 5.050000 |
1 |
| 5.100000 |
2 |
| 5.116667 |
1 |
| 5.133333 |
4 |
| 5.166667 |
2 |
| 5.183333 |
1 |
| 5.233333 |
1 |
| 5.250000 |
1 |
| 5.300000 |
1 |
| 5.316667 |
1 |
| 5.333333 |
2 |
| 5.400000 |
2 |
| 5.416667 |
4 |
| 5.433333 |
1 |
| 5.483333 |
2 |
| 5.500000 |
1 |
| 5.516667 |
1 |
| 5.533333 |
1 |
| 5.550000 |
1 |
| 5.583333 |
1 |
| 5.616667 |
1 |
| 5.633333 |
1 |
| 5.666667 |
1 |
| 5.683333 |
2 |
| 5.700000 |
1 |
| 5.716667 |
1 |
| 5.733333 |
2 |
| 5.800000 |
2 |
| 5.816667 |
1 |
| 5.883333 |
1 |
| 5.950000 |
1 |
| 5.983333 |
1 |
| 6.000000 |
1 |
| 6.016667 |
1 |
| 6.033333 |
1 |
| 6.050000 |
1 |
| 6.083333 |
1 |
| 6.100000 |
1 |
| 6.116667 |
1 |
| 6.133333 |
2 |
| 6.150000 |
1 |
| 6.166667 |
1 |
| 6.216667 |
1 |
| 6.333333 |
1 |
| 6.366667 |
1 |
| 6.383333 |
1 |
| 6.450000 |
2 |
| 6.566667 |
1 |
| 6.800000 |
1 |
| 6.916667 |
1 |
| NA |
10 |
pppp <- ggplot(data = data, mapping = aes(x = hsc_T3, fill = factor(hsc_T3))) + geom_histogram(binwidth = 0.1) #視覚化
ggplotly(pppp) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc_T4の度数分布とヒストグラム
hsc_T4_count <- dplyr::count(data, hsc_T4)
knitr::kable(hsc_T4_count) #テーブル化
| 2.616667 |
1 |
| 3.083333 |
1 |
| 3.183333 |
1 |
| 3.583333 |
1 |
| 3.633333 |
1 |
| 3.833333 |
1 |
| 3.850000 |
1 |
| 3.916667 |
1 |
| 3.933333 |
2 |
| 4.066667 |
1 |
| 4.233333 |
2 |
| 4.250000 |
1 |
| 4.333333 |
2 |
| 4.350000 |
1 |
| 4.366667 |
1 |
| 4.516667 |
1 |
| 4.533333 |
1 |
| 4.616667 |
3 |
| 4.633333 |
2 |
| 4.650000 |
1 |
| 4.666667 |
1 |
| 4.716667 |
1 |
| 4.750000 |
1 |
| 4.766667 |
1 |
| 4.783333 |
3 |
| 4.800000 |
1 |
| 4.833333 |
2 |
| 4.850000 |
1 |
| 4.866667 |
2 |
| 4.883333 |
1 |
| 4.900000 |
1 |
| 4.916667 |
1 |
| 4.933333 |
1 |
| 4.950000 |
1 |
| 5.000000 |
1 |
| 5.033333 |
1 |
| 5.100000 |
1 |
| 5.116667 |
2 |
| 5.150000 |
1 |
| 5.166667 |
1 |
| 5.200000 |
1 |
| 5.250000 |
1 |
| 5.300000 |
2 |
| 5.316667 |
2 |
| 5.333333 |
1 |
| 5.350000 |
1 |
| 5.366667 |
1 |
| 5.383333 |
1 |
| 5.400000 |
1 |
| 5.416667 |
1 |
| 5.433333 |
1 |
| 5.466667 |
1 |
| 5.483333 |
1 |
| 5.533333 |
2 |
| 5.550000 |
2 |
| 5.566667 |
1 |
| 5.633333 |
1 |
| 5.683333 |
2 |
| 5.700000 |
1 |
| 5.716667 |
1 |
| 5.733333 |
1 |
| 5.750000 |
2 |
| 5.766667 |
3 |
| 5.783333 |
2 |
| 5.816667 |
1 |
| 5.850000 |
1 |
| 5.866667 |
1 |
| 5.883333 |
1 |
| 5.900000 |
2 |
| 5.966667 |
1 |
| 5.983333 |
1 |
| 6.050000 |
1 |
| 6.116667 |
1 |
| 6.183333 |
1 |
| 6.250000 |
2 |
| 6.283333 |
1 |
| 6.416667 |
1 |
| 6.433333 |
1 |
| 6.450000 |
1 |
| 6.483333 |
1 |
| 6.550000 |
1 |
| 7.000000 |
1 |
| NA |
10 |
qqqq <- ggplot(data = data, mapping = aes(x = hsc_T4, fill = factor(hsc_T4))) + geom_histogram(binwidth = 0.1) #視覚化
ggplotly(qqqq) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#wb_T1の度数分布とヒストグラム
wb_T1_count <- dplyr::count(data, wb_T1)
knitr::kable(wb_T1_count) #テーブル化
| 0.6 |
1 |
| 0.8 |
1 |
| 1.2 |
1 |
| 1.4 |
5 |
| 1.6 |
5 |
| 1.8 |
6 |
| 2.0 |
7 |
| 2.2 |
11 |
| 2.4 |
5 |
| 2.6 |
11 |
| 2.8 |
11 |
| 3.0 |
14 |
| 3.2 |
7 |
| 3.4 |
7 |
| 3.6 |
7 |
| 3.8 |
5 |
| 4.0 |
3 |
| 4.2 |
3 |
| 4.4 |
1 |
| 4.6 |
1 |
| 5.0 |
1 |
| NA |
1 |
rrrr <- ggplot(data = data, mapping = aes(x = wb_T1, fill = factor(wb_T1))) + geom_histogram(binwidth = 0.5) #視覚化
ggplotly(rrrr) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#wb_T2の度数分布とヒストグラム
wb_T2_count <- dplyr::count(data, wb_T2)
knitr::kable(wb_T2_count) #テーブル化
| 1.0 |
1 |
| 1.2 |
1 |
| 1.4 |
1 |
| 1.6 |
5 |
| 1.8 |
6 |
| 2.0 |
4 |
| 2.2 |
7 |
| 2.4 |
7 |
| 2.6 |
8 |
| 2.8 |
2 |
| 3.0 |
15 |
| 3.2 |
10 |
| 3.4 |
7 |
| 3.6 |
2 |
| 3.8 |
7 |
| 4.0 |
4 |
| 4.2 |
2 |
| 4.4 |
5 |
| 4.6 |
2 |
| 5.0 |
3 |
| NA |
15 |
ssss <- ggplot(data = data, mapping = aes(x = wb_T2, fill = factor(wb_T2))) + geom_histogram(binwidth = 0.5) #視覚化
ggplotly(ssss) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#wb_T3の度数分布とヒストグラム
wb_T3_count <- dplyr::count(data, wb_T3)
knitr::kable(wb_T3_count) #テーブル化
| 0.0 |
1 |
| 0.8 |
1 |
| 1.0 |
2 |
| 1.4 |
3 |
| 1.6 |
1 |
| 1.8 |
5 |
| 2.0 |
5 |
| 2.2 |
8 |
| 2.4 |
10 |
| 2.6 |
7 |
| 2.8 |
10 |
| 3.0 |
12 |
| 3.2 |
8 |
| 3.4 |
5 |
| 3.6 |
4 |
| 3.8 |
10 |
| 4.0 |
5 |
| 4.4 |
1 |
| 4.6 |
3 |
| 4.8 |
1 |
| 5.0 |
3 |
| NA |
9 |
tttt <- ggplot(data = data, mapping = aes(x = wb_T3, fill = factor(wb_T3))) + geom_histogram(binwidth = 0.5) #視覚化
ggplotly(tttt) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#wb_T4の度数分布とヒストグラム
wb_T4_count <- dplyr::count(data, wb_T4)
knitr::kable(wb_T4_count) #テーブル化
| 0.2 |
1 |
| 0.6 |
1 |
| 0.8 |
2 |
| 1.0 |
2 |
| 1.2 |
3 |
| 1.4 |
1 |
| 1.6 |
2 |
| 1.8 |
4 |
| 2.0 |
8 |
| 2.2 |
8 |
| 2.4 |
6 |
| 2.6 |
13 |
| 2.8 |
8 |
| 3.0 |
3 |
| 3.2 |
10 |
| 3.4 |
7 |
| 3.6 |
4 |
| 3.8 |
3 |
| 4.0 |
5 |
| 4.2 |
5 |
| 4.4 |
2 |
| 4.6 |
2 |
| 5.0 |
6 |
| NA |
8 |
uuuu <- ggplot(data = data, mapping = aes(x = wb_T4, fill = factor(wb_T4))) + geom_histogram(binwidth = 0.5) #視覚化
ggplotly(uuuu) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#ev_T1の度数分布とヒストグラム
ev_T1_count <- dplyr::count(data, ev_T1)
knitr::kable(ev_T1_count) #テーブル化
| -3.0 |
3 |
| -2.5 |
1 |
| -2.0 |
1 |
| -1.5 |
7 |
| -1.0 |
4 |
| -0.5 |
9 |
| 0.0 |
21 |
| 0.5 |
18 |
| 1.0 |
11 |
| 1.5 |
1 |
| 2.0 |
3 |
| 2.5 |
15 |
| 3.0 |
19 |
| NA |
1 |
vvvv <- ggplot(data = data, mapping = aes(x = ev_T1, fill = factor(ev_T1))) + geom_histogram(binwidth = 0.5) #視覚化
ggplotly(vvvv) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#ev_T2の度数分布とヒストグラム
ev_T2_count <- dplyr::count(data, ev_T2)
knitr::kable(ev_T2_count) #テーブル化
| -3.0 |
3 |
| -2.5 |
5 |
| -2.0 |
1 |
| -1.5 |
3 |
| -1.0 |
4 |
| -0.5 |
3 |
| 0.0 |
22 |
| 0.5 |
10 |
| 1.0 |
9 |
| 1.5 |
3 |
| 2.0 |
6 |
| 2.5 |
12 |
| 3.0 |
14 |
| NA |
19 |
wwww <- ggplot(data = data, mapping = aes(x = ev_T2, fill = factor(ev_T2))) + geom_histogram(binwidth = 0.5) #視覚化
ggplotly(wwww) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#ev_T3の度数分布とヒストグラム
ev_T3_count <- dplyr::count(data, ev_T3)
knitr::kable(ev_T3_count) #テーブル化
| -3.0 |
7 |
| -2.0 |
3 |
| -1.5 |
1 |
| -1.0 |
3 |
| -0.5 |
8 |
| 0.0 |
20 |
| 0.5 |
17 |
| 1.0 |
8 |
| 1.5 |
6 |
| 2.0 |
10 |
| 2.5 |
5 |
| 3.0 |
15 |
| NA |
11 |
panda <- ggplot(data = data, mapping = aes(x = ev_T3, fill = factor(ev_T3))) + geom_histogram(binwidth = 0.5) #視覚化
ggplotly(panda) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#ev_T4の度数分布とヒストグラム
ev_T4_count <- dplyr::count(data, ev_T4)
knitr::kable(ev_T4_count) #テーブル化
| -3.0 |
3 |
| -2.0 |
1 |
| -1.0 |
3 |
| -0.5 |
10 |
| 0.0 |
21 |
| 0.5 |
15 |
| 1.0 |
9 |
| 1.5 |
8 |
| 2.0 |
10 |
| 2.5 |
7 |
| 3.0 |
14 |
| NA |
13 |
yyyy <- ggplot(data = data, mapping = aes(x = ev_T4, fill = factor(ev_T4))) + geom_histogram(binwidth = 0.5) #視覚化
ggplotly(yyyy) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#hsc_onemonthの度数分布とヒストグラム
hsc_onemonth_count <- dplyr::count(data, hsc_onemonth)
knitr::kable(hsc_onemonth_count) #テーブル化
| 3.170833 |
1 |
| 3.533333 |
1 |
| 3.750000 |
1 |
| 3.887500 |
1 |
| 4.033333 |
1 |
| 4.100000 |
1 |
| 4.145833 |
1 |
| 4.262500 |
1 |
| 4.270833 |
1 |
| 4.279167 |
1 |
| 4.437500 |
1 |
| 4.441667 |
1 |
| 4.475000 |
1 |
| 4.504167 |
1 |
| 4.575000 |
2 |
| 4.600000 |
1 |
| 4.604167 |
1 |
| 4.645833 |
1 |
| 4.679167 |
1 |
| 4.750000 |
1 |
| 4.754167 |
1 |
| 4.762500 |
1 |
| 4.775000 |
1 |
| 4.804167 |
1 |
| 4.837500 |
1 |
| 4.883333 |
1 |
| 4.904167 |
1 |
| 4.912500 |
1 |
| 4.962500 |
1 |
| 4.966667 |
1 |
| 4.975000 |
1 |
| 5.008333 |
1 |
| 5.016667 |
1 |
| 5.037500 |
1 |
| 5.041667 |
1 |
| 5.058333 |
1 |
| 5.066667 |
1 |
| 5.079167 |
1 |
| 5.079167 |
1 |
| 5.116667 |
1 |
| 5.175000 |
1 |
| 5.200000 |
1 |
| 5.241667 |
1 |
| 5.270833 |
1 |
| 5.275000 |
1 |
| 5.287500 |
1 |
| 5.304167 |
2 |
| 5.329167 |
1 |
| 5.362500 |
1 |
| 5.383333 |
1 |
| 5.391667 |
1 |
| 5.400000 |
1 |
| 5.404167 |
1 |
| 5.420833 |
1 |
| 5.425000 |
1 |
| 5.533333 |
1 |
| 5.537500 |
1 |
| 5.554167 |
1 |
| 5.591667 |
1 |
| 5.608333 |
1 |
| 5.625000 |
1 |
| 5.637500 |
2 |
| 5.641667 |
1 |
| 5.650000 |
1 |
| 5.729167 |
1 |
| 5.737500 |
1 |
| 5.783333 |
1 |
| 5.795833 |
1 |
| 5.812500 |
1 |
| 5.829167 |
1 |
| 5.875000 |
1 |
| 5.879167 |
1 |
| 5.900000 |
1 |
| 5.987500 |
1 |
| 6.012500 |
1 |
| 6.029167 |
1 |
| 6.054167 |
1 |
| 6.095833 |
1 |
| 6.158333 |
1 |
| 6.237500 |
1 |
| 6.258333 |
1 |
| 6.570833 |
1 |
| 6.687500 |
1 |
| NA |
28 |
zzzz <- ggplot(data = data, mapping = aes(x = hsc_onemonth, fill = factor(hsc_onemonth))) + geom_histogram(binwidth = 0.3) + guides(fill = "none") #視覚化
ggplotly(zzzz) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#wb_onemonthの度数分布とヒストグラム
wb_onemonth_count <- dplyr::count(data, wb_onemonth)
knitr::kable(wb_onemonth_count) #テーブル化
| 1.15 |
1 |
| 1.25 |
1 |
| 1.30 |
1 |
| 1.40 |
1 |
| 1.65 |
1 |
| 1.75 |
1 |
| 1.80 |
1 |
| 1.85 |
1 |
| 1.95 |
1 |
| 2.00 |
1 |
| 2.00 |
1 |
| 2.10 |
2 |
| 2.15 |
1 |
| 2.20 |
1 |
| 2.25 |
2 |
| 2.30 |
1 |
| 2.30 |
1 |
| 2.35 |
1 |
| 2.40 |
3 |
| 2.45 |
1 |
| 2.45 |
2 |
| 2.50 |
3 |
| 2.55 |
1 |
| 2.60 |
1 |
| 2.70 |
1 |
| 2.75 |
1 |
| 2.75 |
1 |
| 2.80 |
4 |
| 2.80 |
1 |
| 2.85 |
4 |
| 2.85 |
2 |
| 2.85 |
2 |
| 2.90 |
1 |
| 2.90 |
1 |
| 2.95 |
2 |
| 2.95 |
3 |
| 3.05 |
4 |
| 3.05 |
1 |
| 3.15 |
1 |
| 3.20 |
1 |
| 3.30 |
1 |
| 3.35 |
2 |
| 3.40 |
1 |
| 3.45 |
1 |
| 3.45 |
1 |
| 3.50 |
1 |
| 3.55 |
1 |
| 3.60 |
2 |
| 3.60 |
1 |
| 3.65 |
1 |
| 3.75 |
1 |
| 3.80 |
1 |
| 3.80 |
1 |
| 3.85 |
1 |
| 4.05 |
1 |
| 4.10 |
1 |
| 4.20 |
2 |
| 4.25 |
2 |
| 4.40 |
1 |
| 4.45 |
2 |
| 4.55 |
1 |
| 4.85 |
1 |
| NA |
26 |
A <- ggplot(data = data, mapping = aes(x = wb_onemonth, fill = factor(wb_onemonth))) + geom_histogram(binwidth = 0.3) + guides(fill = "none") #視覚化
ggplotly(A) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
#ev_onemonthの度数分布とヒストグラム
ev_onemonth_count <- dplyr::count(data, ev_onemonth)
knitr::kable(ev_onemonth_count) #テーブル化
| -1.625 |
1 |
| -1.125 |
1 |
| -0.875 |
1 |
| -0.750 |
2 |
| -0.625 |
1 |
| -0.500 |
1 |
| -0.375 |
1 |
| -0.250 |
3 |
| -0.125 |
2 |
| 0.000 |
4 |
| 0.125 |
6 |
| 0.250 |
7 |
| 0.375 |
2 |
| 0.500 |
4 |
| 0.625 |
2 |
| 0.750 |
6 |
| 0.875 |
3 |
| 1.000 |
2 |
| 1.125 |
2 |
| 1.250 |
3 |
| 1.375 |
5 |
| 1.500 |
5 |
| 1.625 |
3 |
| 1.750 |
4 |
| 1.875 |
3 |
| 2.125 |
2 |
| 2.250 |
2 |
| 2.500 |
1 |
| 2.625 |
2 |
| 2.750 |
1 |
| 2.875 |
2 |
| NA |
30 |
B <- ggplot(data = data, mapping = aes(x = ev_onemonth, fill = factor(ev_onemonth))) + geom_histogram(binwidth = 0.3) + guides(fill = "none") #視覚化
ggplotly(B) #視覚化
## We recommend that you use the dev version of ggplot2 with `ggplotly()`
## Install it with: `devtools::install_github('hadley/ggplot2')`
1-4. 記述統計量
#hsc_T1
hsc_T1_discriptive <-
data %>%
drop_na() %>%
dplyr::summarise(n = n (), #グループの人数を出力
hsc1.T1.mean = mean (hsc1_T1), #hsc1_T1の平均
hsc1.T1.sd = sd (hsc1_T1), #hsc1_T1のSD
hsc2.T1.mean = mean (hsc2_T1),
hsc2.T1.sd = sd (hsc2_T1),
hsc3.T1.mean = mean (hsc3_T1),
hsc3.T1.sd = sd (hsc3_T1),
hsc4.T1.mean = mean (hsc4_T1),
hsc4.T1.sd = sd (hsc4_T1),
hsc5.T1.mean = mean (hsc5_T1),
hsc5.T1.sd = sd (hsc5_T1),
hsc6.T1.mean = mean (hsc6_T1),
hsc6.T1.sd = sd (hsc6_T1),
hsc7.T1.mean = mean (hsc7_T1),
hsc7.T1.sd = sd (hsc7_T1),
hsc8.T1.mean = mean (hsc8_T1),
hsc8.T1.sd = sd (hsc8_T1),
hsc9.T1.mean = mean (hsc9_T1),
hsc9.T1.sd = sd (hsc9_T1),
hsc10.T1.mean = mean (hsc10_T1),
hsc10.T1.sd = sd (hsc10_T1),
hsc11.T1.mean = mean (hsc11_T1),
hsc11.T1.sd = sd (hsc11_T1),
hsc12.T1.mean = mean (hsc4_T1),
hsc12.T1.sd = sd (hsc4_T1),
eoe.mean.T1 = mean (eoe_T1),
eoe.sd.T1 = sd (eoe_T1),
lst.mean.T1 = mean (lst_T1),
lst.sd.T1 = sd (lst_T1),
aes.mean.T1 = mean (aes_T1),
aes.sd.T1 = sd (aes_T1),
hsc.mean.T1 = mean (hsc_T1),
hsc.sd.T1 = sd (hsc_T1))
knitr::kable(hsc_T1_discriptive, digits = 2) #出力
| 79 |
4.68 |
1.38 |
4.75 |
1.55 |
5.7 |
1.22 |
4.52 |
1.7 |
6.25 |
0.85 |
4.95 |
1.35 |
4.68 |
1.82 |
4.15 |
1.46 |
3.59 |
1.42 |
6.52 |
0.78 |
4.97 |
1.52 |
4.52 |
1.7 |
4.58 |
0.95 |
4.86 |
1.39 |
5.79 |
0.6 |
5.08 |
0.73 |
#hsc_T2
hsc_T2_discriptive <-
data %>%
drop_na() %>%
dplyr::summarise(n = n (), #グループの人数を出力
hsc1.T2.mean = mean (hsc1_T2), #hsc1_T2の平均
hsc1.T2.sd = sd (hsc1_T2), #hsc1_T2のSD
hsc2.T2.mean = mean (hsc2_T2),
hsc2.T2.sd = sd (hsc2_T2),
hsc3.T2.mean = mean (hsc3_T2),
hsc3.T2.sd = sd (hsc3_T2),
hsc4.T2.mean = mean (hsc4_T2),
hsc4.T2.sd = sd (hsc4_T2),
hsc5.T2.mean = mean (hsc5_T2),
hsc5.T2.sd = sd (hsc5_T2),
hsc6.T2.mean = mean (hsc6_T2),
hsc6.T2.sd = sd (hsc6_T2),
hsc7.T2.mean = mean (hsc7_T2),
hsc7.T2.sd = sd (hsc7_T2),
hsc8.T2.mean = mean (hsc8_T2),
hsc8.T2.sd = sd (hsc8_T2),
hsc9.T2.mean = mean (hsc9_T2),
hsc9.T2.sd = sd (hsc9_T2),
hsc10.T2.mean = mean (hsc10_T2),
hsc10.T2.sd = sd (hsc10_T2),
hsc11.T2.mean = mean (hsc11_T2),
hsc11.T2.sd = sd (hsc11_T2),
hsc12.T2.mean = mean (hsc4_T2),
hsc12.T2.sd = sd (hsc4_T2),
eoe.mean.T2 = mean (eoe_T2),
eoe.sd.T2 = sd (eoe_T2),
lst.mean.T2 = mean (lst_T2),
lst.sd.T2 = sd (lst_T2),
aes.mean.T2 = mean (aes_T2),
aes.sd.T2 = sd (aes_T2),
hsc.mean.T2 = mean (hsc_T2),
hsc.sd.T2 = sd (hsc_T2))
knitr::kable(hsc_T2_discriptive, digits = 2) #出力
| 79 |
4.73 |
1.17 |
4.92 |
1.44 |
5.73 |
1.17 |
4.71 |
1.63 |
6.18 |
0.98 |
5.06 |
1.24 |
4.81 |
1.71 |
4.46 |
1.29 |
3.8 |
1.4 |
6.42 |
0.93 |
4.92 |
1.53 |
4.71 |
1.63 |
4.75 |
0.94 |
4.92 |
1.39 |
5.77 |
0.65 |
5.15 |
0.76 |
#hsc_T3
hsc_T3_discriptive <-
data %>%
drop_na() %>%
dplyr::summarise(n = n (), #グループの人数を出力
hsc1.T3.mean = mean (hsc1_T3), #hsc1_T3の平均
hsc1.T3.sd = sd (hsc1_T3), #hsc1_T3のSD
hsc2.T3.mean = mean (hsc2_T3),
hsc2.T3.sd = sd (hsc2_T3),
hsc3.T3.mean = mean (hsc3_T3),
hsc3.T3.sd = sd (hsc3_T3),
hsc4.T3.mean = mean (hsc4_T3),
hsc4.T3.sd = sd (hsc4_T3),
hsc5.T3.mean = mean (hsc5_T3),
hsc5.T3.sd = sd (hsc5_T3),
hsc6.T3.mean = mean (hsc6_T3),
hsc6.T3.sd = sd (hsc6_T3),
hsc7.T3.mean = mean (hsc7_T3),
hsc7.T3.sd = sd (hsc7_T3),
hsc8.T3.mean = mean (hsc8_T3),
hsc8.T3.sd = sd (hsc8_T3),
hsc9.T3.mean = mean (hsc9_T3),
hsc9.T3.sd = sd (hsc9_T3),
hsc10.T3.mean = mean (hsc10_T3),
hsc10.T3.sd = sd (hsc10_T3),
hsc11.T3.mean = mean (hsc11_T3),
hsc11.T3.sd = sd (hsc11_T3),
hsc12.T3.mean = mean (hsc4_T3),
hsc12.T3.sd = sd (hsc4_T3),
eoe.mean.T3 = mean (eoe_T3),
eoe.sd.T3 = sd (eoe_T3),
lst.mean.T3 = mean (lst_T3),
lst.sd.T3 = sd (lst_T3),
aes.mean.T3 = mean (aes_T3),
aes.sd.T3 = sd (aes_T3),
hsc.mean.T3 = mean (hsc_T3),
hsc.sd.T3 = sd (hsc_T3))
knitr::kable(hsc_T3_discriptive, digits = 2) #出力
| 79 |
4.78 |
1.17 |
5.08 |
1.54 |
6.06 |
1.1 |
4.97 |
1.49 |
6.32 |
1.01 |
5.32 |
1.39 |
4.63 |
1.88 |
4.67 |
1.49 |
3.87 |
1.5 |
6.57 |
0.69 |
4.86 |
1.55 |
4.97 |
1.49 |
4.91 |
1.01 |
4.97 |
1.43 |
5.93 |
0.61 |
5.27 |
0.77 |
#hsc_T4
hsc_T4_discriptive <-
data %>%
drop_na() %>%
dplyr::summarise(n = n (), #グループの人数を出力
hsc1.T4.mean = mean (hsc1_T4), #hsc1_T4の平均
hsc1.T4.sd = sd (hsc1_T4), #hsc1_T4のSD
hsc2.T4.mean = mean (hsc2_T4),
hsc2.T4.sd = sd (hsc2_T4),
hsc3.T4.mean = mean (hsc3_T4),
hsc3.T4.sd = sd (hsc3_T4),
hsc4.T4.mean = mean (hsc4_T4),
hsc4.T4.sd = sd (hsc4_T4),
hsc5.T4.mean = mean (hsc5_T4),
hsc5.T4.sd = sd (hsc5_T4),
hsc6.T4.mean = mean (hsc6_T4),
hsc6.T4.sd = sd (hsc6_T4),
hsc7.T4.mean = mean (hsc7_T4),
hsc7.T4.sd = sd (hsc7_T4),
hsc8.T4.mean = mean (hsc8_T4),
hsc8.T4.sd = sd (hsc8_T4),
hsc9.T4.mean = mean (hsc9_T4),
hsc9.T4.sd = sd (hsc9_T4),
hsc10.T4.mean = mean (hsc10_T4),
hsc10.T4.sd = sd (hsc10_T4),
hsc11.T4.mean = mean (hsc11_T4),
hsc11.T4.sd = sd (hsc11_T4),
hsc12.T4.mean = mean (hsc4_T4),
hsc12.T4.sd = sd (hsc4_T4),
eoe.mean.T4 = mean (eoe_T4),
eoe.sd.T4 = sd (eoe_T4),
lst.mean.T4 = mean (lst_T4),
lst.sd.T4 = sd (lst_T4),
aes.mean.T4 = mean (aes_T4),
aes.sd.T4 = sd (aes_T4),
hsc.mean.T4 = mean (hsc_T4),
hsc.sd.T4 = sd (hsc_T4))
knitr::kable(hsc_T4_discriptive, digits = 2) #出力
| 79 |
4.82 |
1.15 |
5.05 |
1.5 |
6.11 |
1.07 |
4.86 |
1.65 |
6.24 |
1.06 |
5.09 |
1.37 |
4.8 |
1.86 |
4.71 |
1.43 |
3.92 |
1.48 |
6.59 |
0.69 |
4.82 |
1.63 |
4.86 |
1.65 |
4.85 |
1.11 |
4.94 |
1.45 |
5.94 |
0.67 |
5.24 |
0.78 |
#hsc_onemonth
hsc_onemonth_discriptive <-
data %>%
drop_na() %>%
dplyr::summarise(n = n (), #グループの人数を出力
hsc1.onemonth.mean = mean (hsc_onemonth), #hsc_onemonthの平均
hsc1.onemonth.sd = sd (hsc_onemonth)) #hsc_onemonthのSD
knitr::kable(hsc_onemonth_discriptive, digits = 2) #出力
#wb_T1
wb_T1_discriptive <-
data %>%
drop_na() %>%
dplyr::summarise(n = n (), #グループの人数を出力
wb1.T1.mean = mean (wb1_T1), #hsc1_T10の平均
wb1.T1.sd = sd (wb1_T1), #hsc1_T10のSD
wb2.T1.mean = mean (wb2_T1),
wb2.T1.sd = sd (wb2_T1),
wb3.T1.mean = mean (wb3_T1),
wb3.T1.sd = sd (wb3_T1),
wb4.T1.mean = mean (wb4_T1),
wb4.T1.sd = sd (wb4_T1),
wb5.T1.mean = mean (wb5_T1),
wb5.T1.sd = sd (wb5_T1),
wb.T1.mean = mean (wb_T1),
wb.T1.sd = sd (wb_T1))
knitr::kable(wb_T1_discriptive, digits = 2) #出力
| 79 |
3.22 |
1 |
2.87 |
1.14 |
3.04 |
1.11 |
2.23 |
1.17 |
2.86 |
1.14 |
2.84 |
0.8 |
#wb_T2
wb_T2_discriptive <-
data %>%
drop_na() %>%
dplyr::summarise(n = n (), #グループの人数を出力
wb1.T2.mean = mean (wb1_T2), #hsc1_T2の平均
wb1.T2.sd = sd (wb1_T2), #hsc1_T2のSD
wb2.T2.mean = mean (wb2_T2),
wb2.T2.sd = sd (wb2_T2),
wb3.T2.mean = mean (wb3_T2),
wb3.T2.sd = sd (wb3_T2),
wb4.T2.mean = mean (wb4_T2),
wb4.T2.sd = sd (wb4_T2),
wb5.T2.mean = mean (wb5_T2),
wb5.T2.sd = sd (wb5_T2),
wb.T2.mean = mean (wb_T2),
wb.T2.sd = sd (wb_T2))
knitr::kable(wb_T2_discriptive, digits = 2) #出力
| 79 |
3.22 |
1 |
3.13 |
1.11 |
3.15 |
1.17 |
2.38 |
1.29 |
2.94 |
1.24 |
2.96 |
0.93 |
#wb_T3
wb_T3_discriptive <-
data %>%
drop_na() %>%
dplyr::summarise(n = n (), #グループの人数を出力
wb1.T3.mean = mean (wb1_T3), #hsc1_T3の平均
wb1.T3.sd = sd (wb1_T3), #hsc1_T3のSD
wb2.T3.mean = mean (wb2_T3),
wb2.T3.sd = sd (wb2_T3),
wb3.T3.mean = mean (wb3_T3),
wb3.T3.sd = sd (wb3_T3),
wb4.T3.mean = mean (wb4_T3),
wb4.T3.sd = sd (wb4_T3),
wb5.T3.mean = mean (wb5_T3),
wb5.T3.sd = sd (wb5_T3),
wb.T3.mean = mean (wb_T3),
wb.T3.sd = sd (wb_T3))
knitr::kable(wb_T3_discriptive, digits = 2) #出力
| 79 |
3.35 |
1.16 |
3.04 |
1.24 |
3.28 |
1.12 |
2.32 |
1.25 |
2.68 |
1.16 |
2.93 |
0.92 |
#wb_T4
wb_T4_discriptive <-
data %>%
drop_na() %>%
dplyr::summarise(n = n (), #グループの人数を出力
wb1.T4.mean = mean (wb1_T4), #hsc1_T4の平均
wb1.T4.sd = sd (wb1_T4), #hsc1_T4のSD
wb2.T4.mean = mean (wb2_T4),
wb2.T4.sd = sd (wb2_T4),
wb3.T4.mean = mean (wb3_T4),
wb3.T4.sd = sd (wb3_T4),
wb4.T4.mean = mean (wb4_T4),
wb4.T4.sd = sd (wb4_T4),
wb5.T4.mean = mean (wb5_T4),
wb5.T4.sd = sd (wb5_T4),
wb.T4.mean = mean (wb_T4),
wb.T4.sd = sd (wb_T4))
knitr::kable(wb_T4_discriptive, digits = 2) #出力
| 79 |
3.18 |
1.11 |
2.91 |
1.21 |
3.15 |
1.22 |
2.33 |
1.32 |
2.89 |
1.37 |
2.89 |
1.03 |
#wb_onemonth
wb_onemonth_discriptive <-
data %>%
drop_na() %>%
dplyr::summarise(n = n (), #グループの人数を出力
wb.onemonth.mean = mean (wb_onemonth), #wb_onemonthの平均
wb.onemonth.sd = sd (wb_onemonth)) #wb_onemonthのSD
knitr::kable(wb_onemonth_discriptive, digits = 2) #出力
#ev_T1
ev_T1_discriptive <-
data %>%
drop_na() %>%
dplyr::summarise(n = n (), #グループの人数を出力
ev1.T1.mean = mean (ev1_T1), #ev1_T1の平均
ev1.T1.sd = sd (ev1_T1), #ev1_T1のSD
ev2.T1.mean = mean (ev2_T1),
ev2.T1.sd = sd (ev2_T1),
ev.T1.mean = mean (ev_T1),
ev.T1.sd = sd (ev_T1))
knitr::kable(ev_T1_discriptive, digits = 2) #出力
| 79 |
1.03 |
2.36 |
1.03 |
2.42 |
1.03 |
1.6 |
#ev_T2
ev_T2_discriptive <-
data %>%
drop_na() %>%
dplyr::summarise(n = n (), #グループの人数を出力
ev1.T2.mean = mean (ev1_T2), #ev1_T2の平均
ev1.T2.sd = sd (ev1_T2), #ev1_T2のSD
ev2.T2.mean = mean (ev2_T2),
ev2.T2.sd = sd (ev2_T2),
ev.T2.mean = mean (ev_T2),
ev.T2.sd = sd (ev_T2))
knitr::kable(ev_T2_discriptive, digits = 2) #出力
| 79 |
1.29 |
2.27 |
0.2 |
2.48 |
0.75 |
1.62 |
#ev_T3
ev_T3_discriptive <-
data %>%
drop_na() %>%
dplyr::summarise(n = n (), #グループの人数を出力
ev1.T3.mean = mean (ev_T3), #ev1_T3の平均
ev1.T3.sd = sd (ev1_T3), #ev1_T3のSD
ev2.T3.mean = mean (ev2_T3),
ev2.T3.sd = sd (ev2_T3),
ev.T3.mean = mean (ev_T3),
ev.T3.sd = sd (ev_T3))
knitr::kable(ev_T3_discriptive, digits = 2) #出力
| 79 |
0.73 |
2.38 |
0.53 |
2.43 |
0.73 |
1.58 |
#ev_T4
ev_T4_discriptive <-
data %>%
drop_na() %>%
dplyr::summarise(n = n (), #グループの人数を出力
ev1.T4.mean = mean (ev1_T4), #ev1_T4の平均
ev1.T4.sd = sd (ev1_T4), #ev1_T4のSD
ev2.T4.mean = mean (ev2_T4),
ev2.T4.sd = sd (ev2_T4),
ev.T4.mean = mean (ev_T4),
ev.T4.sd = sd (ev_T4))
knitr::kable(ev_T4_discriptive, digits = 2) #出力
| 79 |
1.33 |
2.27 |
0.52 |
2.3 |
0.92 |
1.47 |
#ev_onemonth
ev_onemonth_discriptive <-
data %>%
drop_na() %>%
dplyr::summarise(n = n (), #グループの人数を出力
ev.onemonth.mean = mean (ev_onemonth), #ev_onemonthの平均
ev.onemonth.sd = sd (ev_onemonth)) #ev_onemonthのSD
knitr::kable(ev_onemonth_discriptive, digits = 2) #出力
#age_T1
age_T1_discriptive <-
data %>%
drop_na() %>%
dplyr::summarise(n = n (), #グループの人数を出力
age.T1.mean = mean (age_T1), #age_T1の平均
age.T1.sd = sd (age_T1)) #age_T1のSD
knitr::kable(age_T1_discriptive, digits = 2) #出力
#age_T2
age_T2_discriptive <-
data %>%
drop_na() %>%
dplyr::summarise(n = n (), #グループの人数を出力
age.T2.mean = mean (age_T2), #age_T2の平均
age.T2.sd = sd (age_T2)) #age_T2のSD
knitr::kable(age_T2_discriptive, digits = 2) #出力
#age_T3
age_T3_discriptive <-
data %>%
drop_na() %>%
dplyr::summarise(n = n (), #グループの人数を出力
age.T3.mean = mean (age_T3), #age_T3の平均
age.T3.sd = sd (age_T3)) #age_T3のSD
knitr::kable(age_T3_discriptive, digits = 2) #出力
#age_T4
age_T4_discriptive <-
data %>%
drop_na() %>%
dplyr::summarise(n = n (), #グループの人数を出力
age.T4.mean = mean (age_T4), #age_T4の平均
age.T4.sd = sd (age_T4)) #age_T4のSD
knitr::kable(age_T4_discriptive, digits = 2) #出力
1-5. 内的整合性
library(psych)
##
## Attaching package: 'psych'
## The following objects are masked from 'package:ggplot2':
##
## %+%, alpha
library(GPArotation)
#hsc_T1
alpha(data[, c(6,7,8,9,11,12,13,14,15,16,17)]) #alpha .63
##
## Reliability analysis
## Call: alpha(x = data[, c(6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17)])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.64 0.63 0.69 0.14 1.7 0.048 4.9 0.68 0.13
##
## lower alpha upper 95% confidence boundaries
## 0.55 0.64 0.74
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## hsc1_T1 0.66 0.65 0.69 0.16 1.9 0.047 0.024 0.129
## hsc2_T1 0.58 0.57 0.61 0.12 1.3 0.057 0.020 0.094
## hsc3_T1 0.64 0.64 0.69 0.15 1.8 0.048 0.028 0.140
## hsc4_T1 0.59 0.58 0.65 0.12 1.4 0.056 0.027 0.094
## hsc6_T1 0.59 0.58 0.63 0.12 1.4 0.056 0.021 0.105
## hsc7_T1 0.67 0.65 0.70 0.16 1.9 0.044 0.025 0.145
## hsc8_T1 0.56 0.55 0.61 0.11 1.2 0.059 0.019 0.094
## hsc9_T1 0.62 0.62 0.68 0.14 1.6 0.051 0.026 0.129
## hsc10_T1 0.64 0.65 0.69 0.15 1.8 0.049 0.026 0.140
## hsc11_T1 0.59 0.59 0.63 0.12 1.4 0.055 0.022 0.105
## hsc12_T1 0.62 0.61 0.67 0.14 1.6 0.051 0.025 0.105
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## hsc1_T1 114 0.27 0.28 0.142 0.087 4.8 1.38
## hsc2_T1 114 0.63 0.63 0.643 0.467 4.7 1.62
## hsc3_T1 113 0.31 0.34 0.193 0.139 5.6 1.31
## hsc4_T1 114 0.60 0.57 0.506 0.416 4.4 1.78
## hsc6_T1 114 0.58 0.59 0.561 0.437 4.9 1.41
## hsc7_T1 114 0.29 0.26 0.095 0.063 4.8 1.74
## hsc8_T1 114 0.70 0.68 0.700 0.581 4.1 1.38
## hsc9_T1 114 0.44 0.43 0.317 0.264 3.6 1.43
## hsc10_T1 114 0.21 0.30 0.152 0.111 6.5 0.77
## hsc11_T1 114 0.58 0.57 0.551 0.410 4.9 1.51
## hsc12_T1 114 0.46 0.46 0.360 0.286 5.6 1.43
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## hsc1_T1 0.02 0.07 0.09 0.14 0.42 0.17 0.10 0.00
## hsc2_T1 0.03 0.11 0.09 0.18 0.25 0.21 0.14 0.00
## hsc3_T1 0.00 0.04 0.04 0.11 0.20 0.31 0.31 0.01
## hsc4_T1 0.07 0.11 0.11 0.17 0.25 0.15 0.14 0.00
## hsc6_T1 0.02 0.04 0.11 0.18 0.31 0.22 0.12 0.00
## hsc7_T1 0.04 0.10 0.11 0.18 0.15 0.23 0.19 0.00
## hsc8_T1 0.03 0.14 0.11 0.37 0.21 0.11 0.04 0.00
## hsc9_T1 0.08 0.18 0.16 0.33 0.15 0.09 0.01 0.00
## hsc10_T1 0.00 0.00 0.00 0.03 0.09 0.25 0.63 0.00
## hsc11_T1 0.02 0.04 0.11 0.22 0.23 0.20 0.18 0.00
## hsc12_T1 0.02 0.04 0.04 0.02 0.25 0.32 0.31 0.00
omega(data[, c(6,7,8,9,11,12,13,14,15,16,17)],3,fm="ml") #omega hierarchical=.41, omega total=.74

## Omega
## Call: omega(m = data[, c(6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17)],
## nfactors = 3, fm = "ml")
## Alpha: 0.63
## G.6: 0.69
## Omega Hierarchical: 0.41
## Omega H asymptotic: 0.56
## Omega Total 0.74
##
## Schmid Leiman Factor loadings greater than 0.2
## g F1* F2* F3* h2 u2 p2
## hsc1_T1 0.98 1.00 0.00 0.03
## hsc2_T1 0.66 0.69 0.91 0.09 0.48
## hsc3_T1 0.06 0.94 0.20
## hsc4_T1 0.30 0.32 0.22 0.78 0.42
## hsc6_T1 0.43 0.45 0.40 0.60 0.47
## hsc7_T1 0.03 0.97 0.00
## hsc8_T1 0.56 0.67 0.76 0.24 0.41
## hsc9_T1 0.23 0.33 0.17 0.83 0.31
## hsc10_T1 0.25 0.10 0.90 0.09
## hsc11_T1 0.48 0.44 0.46 0.54 0.50
## hsc12_T1 0.23 -0.21 0.15 0.85 0.36
##
## With eigenvalues of:
## g F1* F2* F3*
## 1.41 0.98 0.76 1.09
##
## general/max 1.29 max/min = 1.43
## mean percent general = 0.3 with sd = 0.19 and cv of 0.63
## Explained Common Variance of the general factor = 0.33
##
## The degrees of freedom are 25 and the fit is 0.24
## The number of observations was 114 with Chi Square = 26.06 with prob < 0.4
## The root mean square of the residuals is 0.05
## The df corrected root mean square of the residuals is 0.07
## RMSEA index = 0.031 and the 10 % confidence intervals are 0 0.079
## BIC = -92.35
##
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 44 and the fit is 0.99
## The number of observations was 114 with Chi Square = 106.32 with prob < 4.5e-07
## The root mean square of the residuals is 0.12
## The df corrected root mean square of the residuals is 0.13
##
## RMSEA index = 0.116 and the 10 % confidence intervals are 0.085 0.139
## BIC = -102.07
##
## Measures of factor score adequacy
## g F1* F2* F3*
## Correlation of scores with factors 0.75 0.75 0.75 0.99
## Multiple R square of scores with factors 0.57 0.57 0.56 0.98
## Minimum correlation of factor score estimates 0.13 0.14 0.13 0.97
##
## Total, General and Subset omega for each subset
## g F1* F2* F3*
## Omega total for total scores and subscales 0.74 0.61 0.69 0.45
## Omega general for total scores and subscales 0.41 0.24 0.31 0.10
## Omega group for total scores and subscales 0.26 0.37 0.38 0.35
#hsc_T2
alpha(data[, c(27,28,29,30,32,33,34,35,36,37,38)]) #alpha .78
##
## Reliability analysis
## Call: alpha(x = data[, c(27, 28, 29, 30, 32, 33, 34, 35, 36, 37, 38)])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.78 0.78 0.82 0.24 3.5 0.03 4.9 0.81 0.23
##
## lower alpha upper 95% confidence boundaries
## 0.72 0.78 0.84
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## hsc1_T2 0.77 0.77 0.81 0.25 3.4 0.031 0.032 0.24
## hsc2_T2 0.75 0.75 0.79 0.23 3.0 0.035 0.027 0.20
## hsc3_T2 0.78 0.79 0.82 0.27 3.7 0.029 0.030 0.25
## hsc4_T2 0.75 0.76 0.80 0.24 3.1 0.033 0.031 0.20
## hsc6_T2 0.73 0.73 0.77 0.21 2.7 0.037 0.024 0.20
## hsc7_T2 0.79 0.79 0.83 0.27 3.7 0.028 0.030 0.26
## hsc8_T2 0.74 0.74 0.78 0.22 2.9 0.036 0.028 0.20
## hsc9_T2 0.77 0.77 0.82 0.25 3.4 0.031 0.029 0.23
## hsc10_T2 0.78 0.78 0.82 0.27 3.6 0.030 0.031 0.25
## hsc11_T2 0.75 0.75 0.79 0.23 3.0 0.034 0.029 0.20
## hsc12_T2 0.75 0.75 0.79 0.23 2.9 0.034 0.033 0.18
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## hsc1_T2 100 0.49 0.49 0.42 0.36 4.7 1.28
## hsc2_T2 100 0.67 0.66 0.65 0.56 4.8 1.48
## hsc3_T2 100 0.33 0.38 0.28 0.20 5.6 1.29
## hsc4_T2 100 0.63 0.61 0.57 0.49 4.5 1.74
## hsc6_T2 100 0.77 0.77 0.78 0.69 4.8 1.39
## hsc7_T2 100 0.36 0.34 0.22 0.18 4.8 1.71
## hsc8_T2 99 0.74 0.71 0.71 0.63 4.3 1.34
## hsc9_T2 100 0.50 0.48 0.40 0.35 3.8 1.51
## hsc10_T2 100 0.33 0.39 0.29 0.23 6.3 0.98
## hsc11_T2 99 0.66 0.64 0.62 0.54 4.7 1.58
## hsc12_T2 100 0.67 0.67 0.64 0.56 5.5 1.38
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## hsc1_T2 0.02 0.04 0.10 0.20 0.37 0.22 0.05 0.12
## hsc2_T2 0.01 0.08 0.12 0.13 0.30 0.24 0.12 0.12
## hsc3_T2 0.01 0.02 0.03 0.11 0.22 0.33 0.28 0.12
## hsc4_T2 0.07 0.11 0.10 0.11 0.27 0.25 0.09 0.12
## hsc6_T2 0.01 0.08 0.08 0.13 0.40 0.20 0.10 0.12
## hsc7_T2 0.03 0.09 0.09 0.23 0.10 0.27 0.19 0.12
## hsc8_T2 0.00 0.11 0.16 0.23 0.31 0.13 0.05 0.13
## hsc9_T2 0.04 0.20 0.15 0.30 0.17 0.09 0.05 0.12
## hsc10_T2 0.00 0.01 0.02 0.01 0.12 0.31 0.53 0.12
## hsc11_T2 0.02 0.10 0.11 0.14 0.30 0.18 0.14 0.13
## hsc12_T2 0.01 0.05 0.03 0.09 0.22 0.36 0.24 0.12
omega(data[, c(27,28,29,30,32,33,34,35,36,37,38)],3,fm="ml") #omega hierarchical=.64, omega total=.84

## Omega
## Call: omega(m = data[, c(27, 28, 29, 30, 32, 33, 34, 35, 36, 37, 38)],
## nfactors = 3, fm = "ml")
## Alpha: 0.78
## G.6: 0.82
## Omega Hierarchical: 0.64
## Omega H asymptotic: 0.77
## Omega Total 0.84
##
## Schmid Leiman Factor loadings greater than 0.2
## g F1* F2* F3* h2 u2 p2
## hsc1_T2 0.29 0.25 0.16 0.84 0.53
## hsc2_T2 0.57 0.72 0.84 0.16 0.38
## hsc3_T2 0.51 0.29 0.71 0.08
## hsc4_T2 0.52 0.21 0.32 0.68 0.86
## hsc6_T2 0.85 0.33 0.83 0.17 0.87
## hsc7_T2 0.06 0.94 0.36
## hsc8_T2 0.71 0.26 0.57 0.43 0.88
## hsc9_T2 0.48 0.22 -0.21 0.32 0.68 0.72
## hsc10_T2 0.66 0.47 0.53 0.06
## hsc11_T2 0.50 0.55 0.56 0.44 0.46
## hsc12_T2 0.56 0.30 0.44 0.56 0.72
##
## With eigenvalues of:
## g F1* F2* F3*
## 2.77 0.29 0.89 0.89
##
## general/max 3.11 max/min = 3.03
## mean percent general = 0.54 with sd = 0.3 and cv of 0.56
## Explained Common Variance of the general factor = 0.57
##
## The degrees of freedom are 25 and the fit is 0.45
## The number of observations was 114 with Chi Square = 48.23 with prob < 0.0035
## The root mean square of the residuals is 0.06
## The df corrected root mean square of the residuals is 0.08
## RMSEA index = 0.096 and the 10 % confidence intervals are 0.051 0.129
## BIC = -70.17
##
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 44 and the fit is 1.16
## The number of observations was 114 with Chi Square = 124.99 with prob < 1.1e-09
## The root mean square of the residuals is 0.11
## The df corrected root mean square of the residuals is 0.12
##
## RMSEA index = 0.132 and the 10 % confidence intervals are 0.102 0.154
## BIC = -83.4
##
## Measures of factor score adequacy
## g F1* F2* F3*
## Correlation of scores with factors 0.89 0.40 0.84 0.77
## Multiple R square of scores with factors 0.79 0.16 0.71 0.59
## Minimum correlation of factor score estimates 0.59 -0.67 0.41 0.18
##
## Total, General and Subset omega for each subset
## g F1* F2* F3*
## Omega total for total scores and subscales 0.84 0.79 0.74 0.57
## Omega general for total scores and subscales 0.64 0.68 0.33 0.16
## Omega group for total scores and subscales 0.16 0.11 0.41 0.42
#hsc_T3
alpha(data[, c(48,49,50,51,53,54,55,56,57,58,59)]) #alpha .75
## Some items ( hsc3_T3 hsc10_T3 ) were negatively correlated with the total scale and
## probably should be reversed.
## To do this, run the function again with the 'check.keys=TRUE' option
##
## Reliability analysis
## Call: alpha(x = data[, c(48, 49, 50, 51, 53, 54, 55, 56, 57, 58, 59)])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.75 0.73 0.79 0.2 2.8 0.033 5.1 0.72 0.24
##
## lower alpha upper 95% confidence boundaries
## 0.68 0.75 0.81
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## hsc1_T3 0.74 0.73 0.79 0.21 2.6 0.033 0.058 0.25
## hsc2_T3 0.69 0.68 0.74 0.18 2.1 0.040 0.042 0.22
## hsc3_T3 0.77 0.76 0.80 0.24 3.2 0.030 0.045 0.25
## hsc4_T3 0.72 0.71 0.77 0.19 2.4 0.036 0.054 0.21
## hsc6_T3 0.69 0.67 0.73 0.17 2.0 0.041 0.042 0.21
## hsc7_T3 0.75 0.73 0.79 0.21 2.7 0.032 0.056 0.24
## hsc8_T3 0.69 0.67 0.74 0.17 2.1 0.042 0.038 0.21
## hsc9_T3 0.75 0.73 0.79 0.21 2.7 0.033 0.053 0.24
## hsc10_T3 0.76 0.76 0.79 0.24 3.1 0.031 0.044 0.25
## hsc11_T3 0.70 0.69 0.75 0.18 2.2 0.039 0.045 0.22
## hsc12_T3 0.73 0.71 0.76 0.19 2.4 0.035 0.056 0.24
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## hsc1_T3 105 0.42 0.45 0.342 0.288 4.8 1.22
## hsc2_T3 105 0.74 0.71 0.725 0.641 5.1 1.45
## hsc3_T3 105 0.11 0.19 0.054 -0.026 6.0 1.10
## hsc4_T3 105 0.59 0.57 0.509 0.455 5.0 1.45
## hsc6_T3 104 0.77 0.76 0.782 0.690 5.3 1.31
## hsc7_T3 105 0.49 0.43 0.312 0.284 4.8 1.82
## hsc8_T3 105 0.78 0.75 0.777 0.694 4.7 1.46
## hsc9_T3 105 0.45 0.42 0.304 0.280 3.9 1.49
## hsc10_T3 105 0.12 0.22 0.112 0.011 6.4 0.85
## hsc11_T3 105 0.70 0.67 0.672 0.577 4.9 1.50
## hsc12_T3 105 0.52 0.57 0.523 0.419 5.7 1.06
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## hsc1_T3 0.01 0.03 0.11 0.18 0.38 0.23 0.06 0.08
## hsc2_T3 0.02 0.05 0.09 0.10 0.27 0.33 0.14 0.08
## hsc3_T3 0.00 0.01 0.02 0.06 0.22 0.28 0.42 0.08
## hsc4_T3 0.03 0.07 0.03 0.16 0.35 0.23 0.13 0.08
## hsc6_T3 0.02 0.02 0.05 0.13 0.30 0.32 0.16 0.09
## hsc7_T3 0.05 0.10 0.08 0.21 0.12 0.22 0.22 0.08
## hsc8_T3 0.02 0.05 0.14 0.22 0.28 0.17 0.12 0.08
## hsc9_T3 0.06 0.14 0.15 0.28 0.24 0.10 0.04 0.08
## hsc10_T3 0.00 0.00 0.01 0.03 0.10 0.28 0.59 0.08
## hsc11_T3 0.03 0.04 0.10 0.21 0.24 0.24 0.15 0.08
## hsc12_T3 0.00 0.01 0.02 0.09 0.26 0.37 0.26 0.08
omega(data[, c(48,49,50,51,53,54,55,56,57,58,59)],3,fm="ml") #omega hierarchical=.59, omega total=.83

## Omega
## Call: omega(m = data[, c(48, 49, 50, 51, 53, 54, 55, 56, 57, 58, 59)],
## nfactors = 3, fm = "ml")
## Alpha: 0.75
## G.6: 0.8
## Omega Hierarchical: 0.59
## Omega H asymptotic: 0.71
## Omega Total 0.83
##
## Schmid Leiman Factor loadings greater than 0.2
## g F1* F2* F3* h2 u2 p2
## hsc1_T3 0.28 0.11 0.89 0.68
## hsc2_T3 0.96 0.92 0.08 0.99
## hsc3_T3- -0.41 0.17 0.83 0.03
## hsc4_T3 0.32 0.42 0.29 0.71 0.36
## hsc6_T3 0.57 0.71 0.83 0.17 0.39
## hsc7_T3 0.27 0.10 0.90 0.70
## hsc8_T3 0.70 0.45 0.70 0.30 0.71
## hsc9_T3 0.32 0.15 0.85 0.67
## hsc10_T3- -0.99 1.00 0.00 0.01
## hsc11_T3 0.73 0.55 0.45 0.97
## hsc12_T3 0.24 0.44 0.28 0.35 0.65 0.16
##
## With eigenvalues of:
## g F1* F2* F3*
## 2.70 1.19 0.01 1.29
##
## general/max 2.09 max/min = 162.31
## mean percent general = 0.52 with sd = 0.35 and cv of 0.68
## Explained Common Variance of the general factor = 0.52
##
## The degrees of freedom are 25 and the fit is 0.26
## The number of observations was 114 with Chi Square = 27.85 with prob < 0.31
## The root mean square of the residuals is 0.04
## The df corrected root mean square of the residuals is 0.06
## RMSEA index = 0.04 and the 10 % confidence intervals are 0 0.084
## BIC = -90.55
##
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 44 and the fit is 1.38
## The number of observations was 114 with Chi Square = 148.56 with prob < 2.9e-13
## The root mean square of the residuals is 0.13
## The df corrected root mean square of the residuals is 0.14
##
## RMSEA index = 0.15 and the 10 % confidence intervals are 0.12 0.171
## BIC = -59.83
##
## Measures of factor score adequacy
## g F1* F2* F3*
## Correlation of scores with factors 0.97 0.88 0.08 1
## Multiple R square of scores with factors 0.93 0.78 0.01 1
## Minimum correlation of factor score estimates 0.86 0.56 -0.99 1
##
## Total, General and Subset omega for each subset
## g F1* F2* F3*
## Omega total for total scores and subscales 0.83 0.79 0.92 0.71
## Omega general for total scores and subscales 0.59 0.49 0.92 0.01
## Omega group for total scores and subscales 0.26 0.29 0.00 0.70
#hsc_T4
alpha(data[, c(69,70,71,72,74,75,76,77,78,79,80)]) #alpha .79
##
## Reliability analysis
## Call: alpha(x = data[, c(69, 70, 71, 72, 74, 75, 76, 77, 78, 79, 80)])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.79 0.79 0.84 0.25 3.7 0.028 5 0.8 0.22
##
## lower alpha upper 95% confidence boundaries
## 0.74 0.79 0.85
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## hsc1_T4 0.79 0.79 0.84 0.27 3.7 0.028 0.042 0.24
## hsc2_T4 0.77 0.76 0.81 0.24 3.2 0.031 0.036 0.22
## hsc3_T4 0.80 0.80 0.83 0.28 3.9 0.027 0.033 0.24
## hsc4_T4 0.76 0.76 0.81 0.24 3.1 0.032 0.040 0.19
## hsc6_T4 0.75 0.74 0.79 0.23 2.9 0.034 0.032 0.20
## hsc7_T4 0.79 0.78 0.83 0.26 3.6 0.028 0.042 0.22
## hsc8_T4 0.75 0.74 0.79 0.22 2.9 0.034 0.027 0.20
## hsc9_T4 0.78 0.78 0.83 0.26 3.5 0.029 0.042 0.20
## hsc10_T4 0.79 0.79 0.82 0.27 3.7 0.028 0.038 0.24
## hsc11_T4 0.77 0.76 0.81 0.24 3.2 0.031 0.035 0.21
## hsc12_T4 0.75 0.75 0.80 0.23 2.9 0.033 0.036 0.19
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## hsc1_T4 106 0.37 0.41 0.29 0.25 4.8 1.17
## hsc2_T4 106 0.61 0.60 0.57 0.49 5.0 1.44
## hsc3_T4 106 0.28 0.34 0.26 0.15 5.9 1.19
## hsc4_T4 106 0.66 0.65 0.61 0.54 4.8 1.58
## hsc6_T4 106 0.75 0.74 0.75 0.66 5.0 1.45
## hsc7_T4 106 0.50 0.47 0.37 0.33 4.9 1.73
## hsc8_T4 106 0.77 0.75 0.76 0.69 4.6 1.42
## hsc9_T4 106 0.50 0.48 0.39 0.36 3.9 1.45
## hsc10_T4 106 0.36 0.43 0.36 0.26 6.3 0.96
## hsc11_T4 106 0.64 0.62 0.60 0.52 4.8 1.57
## hsc12_T4 106 0.73 0.73 0.71 0.65 5.6 1.35
##
## Non missing response frequency for each item
## 1 2 3 4 5 6 7 miss
## hsc1_T4 0.01 0.02 0.14 0.16 0.39 0.25 0.03 0.07
## hsc2_T4 0.00 0.08 0.10 0.08 0.30 0.30 0.12 0.07
## hsc3_T4 0.00 0.02 0.02 0.08 0.20 0.27 0.41 0.07
## hsc4_T4 0.05 0.05 0.12 0.11 0.33 0.21 0.13 0.07
## hsc6_T4 0.02 0.08 0.08 0.08 0.36 0.27 0.11 0.07
## hsc7_T4 0.04 0.08 0.10 0.15 0.17 0.25 0.20 0.07
## hsc8_T4 0.02 0.05 0.16 0.19 0.32 0.16 0.10 0.07
## hsc9_T4 0.06 0.12 0.18 0.28 0.23 0.10 0.03 0.07
## hsc10_T4 0.00 0.01 0.01 0.03 0.10 0.28 0.57 0.07
## hsc11_T4 0.02 0.09 0.10 0.17 0.26 0.21 0.14 0.07
## hsc12_T4 0.01 0.05 0.02 0.07 0.29 0.29 0.27 0.07
omega(data[, c(69,70,71,72,74,75,76,77,78,79,80)],3,fm="ml") #omega hierarchical=.71, omega total=.86

## Omega
## Call: omega(m = data[, c(69, 70, 71, 72, 74, 75, 76, 77, 78, 79, 80)],
## nfactors = 3, fm = "ml")
## Alpha: 0.79
## G.6: 0.84
## Omega Hierarchical: 0.71
## Omega H asymptotic: 0.83
## Omega Total 0.86
##
## Schmid Leiman Factor loadings greater than 0.2
## g F1* F2* F3* h2 u2 p2
## hsc1_T4 0.22 0.24 0.10 0.90 0.46
## hsc2_T4 0.46 0.79 0.83 0.17 0.25
## hsc3_T4 0.74 0.55 0.45 0.01
## hsc4_T4 0.63 0.43 0.57 0.94
## hsc6_T4 0.83 0.70 0.30 0.99
## hsc7_T4 0.39 0.16 0.84 0.96
## hsc8_T4 0.84 0.23 0.80 0.20 0.89
## hsc9_T4 0.36 0.14 0.86 0.93
## hsc10_T4 0.77 0.61 0.39 0.05
## hsc11_T4 0.50 0.55 0.56 0.44 0.45
## hsc12_T4 0.74 0.56 0.44 0.98
##
## With eigenvalues of:
## g F1* F2* F3*
## 3.17 0.01 1.01 1.24
##
## general/max 2.56 max/min = 95.33
## mean percent general = 0.63 with sd = 0.39 and cv of 0.62
## Explained Common Variance of the general factor = 0.58
##
## The degrees of freedom are 25 and the fit is 0.33
## The number of observations was 114 with Chi Square = 35.47 with prob < 0.08
## The root mean square of the residuals is 0.04
## The df corrected root mean square of the residuals is 0.06
## RMSEA index = 0.067 and the 10 % confidence intervals are 0 0.104
## BIC = -82.93
##
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 44 and the fit is 1.41
## The number of observations was 114 with Chi Square = 152.05 with prob < 8.2e-14
## The root mean square of the residuals is 0.12
## The df corrected root mean square of the residuals is 0.14
##
## RMSEA index = 0.152 and the 10 % confidence intervals are 0.122 0.173
## BIC = -56.35
##
## Measures of factor score adequacy
## g F1* F2* F3*
## Correlation of scores with factors 0.94 0.07 0.88 0.87
## Multiple R square of scores with factors 0.88 0.01 0.78 0.75
## Minimum correlation of factor score estimates 0.76 -0.99 0.55 0.50
##
## Total, General and Subset omega for each subset
## g F1* F2* F3*
## Omega total for total scores and subscales 0.86 0.7 0.78 0.69
## Omega general for total scores and subscales 0.71 0.7 0.64 0.21
## Omega group for total scores and subscales 0.14 0.0 0.14 0.48
#wb_T1
alpha(data[, c(18:22)]) #alpha .78
##
## Reliability analysis
## Call: alpha(x = data[, c(18:22)])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.78 0.78 0.77 0.42 3.6 0.033 2.7 0.83 0.43
##
## lower alpha upper 95% confidence boundaries
## 0.71 0.78 0.84
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## wb1_T1 0.71 0.71 0.68 0.38 2.5 0.044 0.012 0.36
## wb2_T1 0.72 0.73 0.69 0.40 2.7 0.042 0.020 0.39
## wb3_T1 0.72 0.72 0.70 0.39 2.6 0.044 0.019 0.38
## wb4_T1 0.78 0.78 0.75 0.48 3.6 0.033 0.012 0.50
## wb5_T1 0.75 0.75 0.73 0.43 3.0 0.039 0.018 0.44
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## wb1_T1 114 0.77 0.78 0.74 0.63 3.1 1.1
## wb2_T1 114 0.75 0.76 0.70 0.59 2.8 1.1
## wb3_T1 113 0.77 0.77 0.70 0.61 2.9 1.2
## wb4_T1 114 0.64 0.63 0.49 0.42 2.1 1.2
## wb5_T1 114 0.71 0.71 0.60 0.52 2.7 1.2
##
## Non missing response frequency for each item
## 0 1 2 3 4 5 miss
## wb1_T1 0.01 0.05 0.24 0.34 0.27 0.09 0.00
## wb2_T1 0.02 0.11 0.27 0.35 0.20 0.05 0.00
## wb3_T1 0.02 0.10 0.23 0.32 0.25 0.09 0.01
## wb4_T1 0.04 0.31 0.30 0.24 0.08 0.04 0.00
## wb5_T1 0.03 0.13 0.25 0.33 0.19 0.06 0.00
omega(data[, c(18:22)],1,fm="ml") #omega hierarchical=.78, omega total=.79
## Omega_h for 1 factor is not meaningful, just omega_t
## Omega
## Call: omega(m = data[, c(18:22)], nfactors = 1, fm = "ml")
## Alpha: 0.78
## G.6: 0.77
## Omega Hierarchical: 0.78
## Omega H asymptotic: 0.99
## Omega Total 0.79
##
## Schmid Leiman Factor loadings greater than 0.2
## g F1* h2 u2 p2
## wb1_T1 0.79 0.62 0.38 1
## wb2_T1 0.68 0.46 0.54 1
## wb3_T1 0.71 0.50 0.50 1
## wb4_T1 0.44 0.20 0.80 1
## wb5_T1 0.61 0.37 0.63 1
##
## With eigenvalues of:
## g F1*
## 2.1 0.0
##
## general/max 1.933559e+16 max/min = 1
## mean percent general = 1 with sd = 0 and cv of 0
## Explained Common Variance of the general factor = 1
##
## The degrees of freedom are 5 and the fit is 0.23
## The number of observations was 114 with Chi Square = 25.49 with prob < 0.00011
## The root mean square of the residuals is 0.09
## The df corrected root mean square of the residuals is 0.12
## RMSEA index = 0.194 and the 10 % confidence intervals are 0.121 0.267
## BIC = 1.8
##
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 5 and the fit is 0.23
## The number of observations was 114 with Chi Square = 25.49 with prob < 0.00011
## The root mean square of the residuals is 0.09
## The df corrected root mean square of the residuals is 0.12
##
## RMSEA index = 0.194 and the 10 % confidence intervals are 0.121 0.267
## BIC = 1.8
##
## Measures of factor score adequacy
## g F1*
## Correlation of scores with factors 0.90 0
## Multiple R square of scores with factors 0.81 0
## Minimum correlation of factor score estimates 0.63 -1
##
## Total, General and Subset omega for each subset
## g F1*
## Omega total for total scores and subscales 0.79 0.78
## Omega general for total scores and subscales 0.78 0.78
## Omega group for total scores and subscales 0.00 0.00
#wb_T2
alpha(data[, c(39:43)]) #alpha .85
##
## Reliability analysis
## Call: alpha(x = data[, c(39:43)])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.84 0.85 0.85 0.52 5.5 0.024 3 0.91 0.48
##
## lower alpha upper 95% confidence boundaries
## 0.79 0.84 0.89
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## wb1_T2 0.80 0.80 0.77 0.50 4.0 0.031 0.0099 0.46
## wb2_T2 0.79 0.79 0.75 0.49 3.8 0.032 0.0081 0.47
## wb3_T2 0.81 0.81 0.81 0.52 4.4 0.030 0.0204 0.47
## wb4_T2 0.83 0.83 0.82 0.56 5.0 0.026 0.0132 0.54
## wb5_T2 0.82 0.83 0.83 0.55 4.9 0.027 0.0168 0.52
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## wb1_T2 100 0.81 0.82 0.80 0.70 3.2 1.0
## wb2_T2 99 0.83 0.84 0.82 0.73 3.1 1.1
## wb3_T2 100 0.79 0.79 0.72 0.66 3.1 1.1
## wb4_T2 100 0.75 0.73 0.65 0.58 2.4 1.3
## wb5_T2 100 0.75 0.75 0.65 0.59 3.0 1.2
##
## Non missing response frequency for each item
## 0 1 2 3 4 5 miss
## wb1_T2 0.00 0.05 0.23 0.30 0.33 0.09 0.12
## wb2_T2 0.00 0.07 0.23 0.28 0.32 0.09 0.13
## wb3_T2 0.00 0.10 0.16 0.36 0.25 0.13 0.12
## wb4_T2 0.02 0.26 0.29 0.23 0.12 0.08 0.12
## wb5_T2 0.00 0.12 0.26 0.28 0.21 0.13 0.12
omega(data[, c(39:43)],1,fm="ml") #omega hierarchical=.83, omega total=.85
## Omega_h for 1 factor is not meaningful, just omega_t
## Omega
## Call: omega(m = data[, c(39:43)], nfactors = 1, fm = "ml")
## Alpha: 0.85
## G.6: 0.85
## Omega Hierarchical: 0.83
## Omega H asymptotic: 0.98
## Omega Total 0.85
##
## Schmid Leiman Factor loadings greater than 0.2
## g F1* h2 u2 p2
## wb1_T2 0.84 0.71 0.29 1
## wb2_T2 0.86 0.74 0.26 1
## wb3_T2 0.65 0.43 0.57 1
## wb4_T2 0.63 0.40 0.60 1
## wb5_T2 0.60 0.36 0.64 1
##
## With eigenvalues of:
## g F1*
## 2.6 0.0
##
## general/max 4.745101e+16 max/min = 1
## mean percent general = 1 with sd = 0 and cv of 0
## Explained Common Variance of the general factor = 1
##
## The degrees of freedom are 5 and the fit is 0.43
## The number of observations was 114 with Chi Square = 47.58 with prob < 4.3e-09
## The root mean square of the residuals is 0.09
## The df corrected root mean square of the residuals is 0.13
## RMSEA index = 0.279 and the 10 % confidence intervals are 0.207 0.348
## BIC = 23.9
##
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 5 and the fit is 0.43
## The number of observations was 114 with Chi Square = 47.58 with prob < 4.3e-09
## The root mean square of the residuals is 0.09
## The df corrected root mean square of the residuals is 0.13
##
## RMSEA index = 0.279 and the 10 % confidence intervals are 0.207 0.348
## BIC = 23.9
##
## Measures of factor score adequacy
## g F1*
## Correlation of scores with factors 0.94 0
## Multiple R square of scores with factors 0.88 0
## Minimum correlation of factor score estimates 0.76 -1
##
## Total, General and Subset omega for each subset
## g F1*
## Omega total for total scores and subscales 0.85 0.83
## Omega general for total scores and subscales 0.83 0.83
## Omega group for total scores and subscales 0.00 0.00
#wb_T3
alpha(data[, c(60:64)]) #alpha .85
##
## Reliability analysis
## Call: alpha(x = data[, c(60:64)])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.85 0.85 0.85 0.54 5.8 0.022 2.9 0.94 0.51
##
## lower alpha upper 95% confidence boundaries
## 0.81 0.85 0.9
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## wb1_T3 0.81 0.81 0.78 0.52 4.4 0.029 0.0065 0.50
## wb2_T3 0.81 0.82 0.77 0.53 4.4 0.028 0.0029 0.51
## wb3_T3 0.83 0.83 0.81 0.55 4.8 0.026 0.0105 0.51
## wb4_T3 0.83 0.83 0.81 0.55 4.9 0.027 0.0109 0.53
## wb5_T3 0.83 0.83 0.81 0.55 4.9 0.026 0.0102 0.52
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## wb1_T3 105 0.82 0.82 0.78 0.71 3.3 1.1
## wb2_T3 105 0.82 0.81 0.78 0.70 3.0 1.2
## wb3_T3 105 0.78 0.78 0.71 0.65 3.2 1.2
## wb4_T3 105 0.79 0.78 0.70 0.65 2.3 1.3
## wb5_T3 105 0.77 0.77 0.69 0.64 2.7 1.1
##
## Non missing response frequency for each item
## 0 1 2 3 4 5 miss
## wb1_T3 0.01 0.06 0.17 0.34 0.27 0.15 0.08
## wb2_T3 0.01 0.11 0.21 0.30 0.26 0.10 0.08
## wb3_T3 0.01 0.08 0.16 0.33 0.28 0.14 0.08
## wb4_T3 0.06 0.19 0.38 0.17 0.14 0.06 0.08
## wb5_T3 0.01 0.15 0.26 0.36 0.15 0.07 0.08
omega(data[, c(60:64)],1,fm="ml") #omega hierarchical=.85, omega total=.85
## Omega_h for 1 factor is not meaningful, just omega_t
## Omega
## Call: omega(m = data[, c(60:64)], nfactors = 1, fm = "ml")
## Alpha: 0.85
## G.6: 0.85
## Omega Hierarchical: 0.85
## Omega H asymptotic: 0.99
## Omega Total 0.85
##
## Schmid Leiman Factor loadings greater than 0.2
## g F1* h2 u2 p2
## wb1_T3 0.81 0.66 0.34 1
## wb2_T3 0.80 0.65 0.35 1
## wb3_T3 0.68 0.47 0.53 1
## wb4_T3 0.70 0.49 0.51 1
## wb5_T3 0.66 0.44 0.56 1
##
## With eigenvalues of:
## g F1*
## 2.7 0.0
##
## general/max Inf max/min = NaN
## mean percent general = 1 with sd = 0 and cv of 0
## Explained Common Variance of the general factor = 1
##
## The degrees of freedom are 5 and the fit is 0.3
## The number of observations was 114 with Chi Square = 32.82 with prob < 4.1e-06
## The root mean square of the residuals is 0.08
## The df corrected root mean square of the residuals is 0.11
## RMSEA index = 0.226 and the 10 % confidence intervals are 0.153 0.297
## BIC = 9.14
##
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 5 and the fit is 0.3
## The number of observations was 114 with Chi Square = 32.82 with prob < 4.1e-06
## The root mean square of the residuals is 0.08
## The df corrected root mean square of the residuals is 0.11
##
## RMSEA index = 0.226 and the 10 % confidence intervals are 0.153 0.297
## BIC = 9.14
##
## Measures of factor score adequacy
## g F1*
## Correlation of scores with factors 0.93 0
## Multiple R square of scores with factors 0.86 0
## Minimum correlation of factor score estimates 0.73 -1
##
## Total, General and Subset omega for each subset
## g F1*
## Omega total for total scores and subscales 0.85 0.85
## Omega general for total scores and subscales 0.85 0.85
## Omega group for total scores and subscales 0.00 0.00
#wb_T4
alpha(data[, c(81:85)]) #alpha .90
##
## Reliability analysis
## Call: alpha(x = data[, c(81:85)])
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.9 0.9 0.9 0.63 8.7 0.016 2.9 1.1 0.63
##
## lower alpha upper 95% confidence boundaries
## 0.86 0.9 0.93
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## wb1_T4 0.86 0.86 0.86 0.61 6.3 0.021 0.0115 0.58
## wb2_T4 0.87 0.87 0.84 0.62 6.6 0.021 0.0053 0.63
## wb3_T4 0.88 0.88 0.87 0.65 7.5 0.018 0.0076 0.63
## wb4_T4 0.88 0.88 0.86 0.64 7.2 0.019 0.0051 0.68
## wb5_T4 0.87 0.88 0.87 0.64 7.1 0.020 0.0123 0.64
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## wb1_T4 106 0.86 0.87 0.83 0.79 3.1 1.1
## wb2_T4 106 0.86 0.86 0.84 0.77 2.8 1.3
## wb3_T4 106 0.81 0.81 0.75 0.70 3.1 1.2
## wb4_T4 106 0.83 0.83 0.79 0.73 2.3 1.3
## wb5_T4 106 0.84 0.83 0.77 0.73 2.9 1.3
##
## Non missing response frequency for each item
## 0 1 2 3 4 5 miss
## wb1_T4 0.01 0.09 0.16 0.32 0.32 0.09 0.07
## wb2_T4 0.03 0.13 0.23 0.30 0.20 0.11 0.07
## wb3_T4 0.01 0.08 0.24 0.29 0.22 0.16 0.07
## wb4_T4 0.06 0.23 0.32 0.20 0.11 0.08 0.07
## wb5_T4 0.04 0.11 0.24 0.28 0.19 0.14 0.07
omega(data[, c(81:85)],1,fm="ml") #omega hierarchical=.90, omega total=.90
## Omega_h for 1 factor is not meaningful, just omega_t
## Omega
## Call: omega(m = data[, c(81:85)], nfactors = 1, fm = "ml")
## Alpha: 0.9
## G.6: 0.9
## Omega Hierarchical: 0.9
## Omega H asymptotic: 1
## Omega Total 0.9
##
## Schmid Leiman Factor loadings greater than 0.2
## g F1* h2 u2 p2
## wb1_T4 0.84 0.71 0.29 1
## wb2_T4 0.84 0.71 0.29 1
## wb3_T4 0.74 0.55 0.45 1
## wb4_T4 0.79 0.62 0.38 1
## wb5_T4 0.77 0.59 0.41 1
##
## With eigenvalues of:
## g F1*
## 3.2 0.0
##
## general/max Inf max/min = NaN
## mean percent general = 1 with sd = 0 and cv of 0
## Explained Common Variance of the general factor = 1
##
## The degrees of freedom are 5 and the fit is 0.43
## The number of observations was 114 with Chi Square = 46.84 with prob < 6.1e-09
## The root mean square of the residuals is 0.08
## The df corrected root mean square of the residuals is 0.11
## RMSEA index = 0.276 and the 10 % confidence intervals are 0.204 0.346
## BIC = 23.16
##
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 5 and the fit is 0.43
## The number of observations was 114 with Chi Square = 46.84 with prob < 6.1e-09
## The root mean square of the residuals is 0.08
## The df corrected root mean square of the residuals is 0.11
##
## RMSEA index = 0.276 and the 10 % confidence intervals are 0.204 0.346
## BIC = 23.16
##
## Measures of factor score adequacy
## g F1*
## Correlation of scores with factors 0.95 0
## Multiple R square of scores with factors 0.90 0
## Minimum correlation of factor score estimates 0.80 -1
##
## Total, General and Subset omega for each subset
## g F1*
## Omega total for total scores and subscales 0.9 0.9
## Omega general for total scores and subscales 0.9 0.9
## Omega group for total scores and subscales 0.0 0.0